A biomechanics and energetics dataset of neurotypical adults walking with and without kinematic constraints

ABSTRACT Numerous studies have explored the biomechanics and energetics of human walking, offering valuable insights into how we walk. However, prior studies focused on changing external

factors (e.g., walking speed) and examined group averages and trends rather than individual adaptations in the presence of internal constraints (e.g., injury-related muscle weakness). To

address this gap, this paper presents an open dataset of human walking biomechanics and energetics collected from 21 neurotypical young adults. To investigate the effects of internal

constraints (reduced joint range of motion), the participants are both the _control_ group (free walking) and the _intervention_ group (constrained walking - left knee fully extended using a

passive orthosis). Each subject walked on a dual-belt treadmill at three speeds (0.4, 0.8, and 1.1 m/s) and five step frequencies ( − 10% to 20% of their preferred frequency) for a total of

30 test conditions. The dataset includes raw and segmented data featuring ground reaction forces, joint motion, muscle activity, and metabolic data. Additionally, a sample code is provided

for basic data manipulation and visualisation. SIMILAR CONTENT BEING VIEWED BY OTHERS A BIOMECHANICS DATASET OF HEALTHY HUMAN WALKING AT VARIOUS SPEEDS, STEP LENGTHS AND STEP WIDTHS Article

Open access 16 November 2022 LOWER-LIMB KINEMATICS AND KINETICS DURING CONTINUOUSLY VARYING HUMAN LOCOMOTION Article Open access 28 October 2021 DATASET OF WALKING AND RUNNING BIOMECHANICS

WITH DIFFERENT STEP WIDTHS ACROSS DIFFERENT SPEEDS Article Open access 16 May 2025 BACKGROUND & SUMMARY Gait analysis is an essential tool for understanding human walking. Previous

studies have demonstrated that humans optimise their gait, but the optimisation criteria may vary with individual health status and environmental conditions. Neurotypical adults tend to walk

efficiently1,2,3 as predicted by simple pendulum models4,5,6,7. This is a consequence of the adaptations they make when walking at different speeds8,9,10,11,12,13, in the presence of

changing external conditions14,15,16,17, and as they age13,18,19,20,21,22,23. Similar can be seen in people with physical impairments as they also adapt their gait24,25,26,27,28,29 albeit

for different reasons than neurotypical individuals30,31,32,33,34. However, little is known about human adaptations on an individual level for several reasons. First, human studies are

resource-intensive to conduct, making access to much-needed data severely limited (particularly for neurologically-impaired patients). Furthermore, the findings cited above come from studies

designed-for-purpose and not publicly shared. Second, studies seldom investigate the effects of constraining normally available gait mechanisms (_internal_ constraints; e.g., reducing

muscle strength). One recent study that did35 has a small sample size and a small number of walking conditions tested (in terms of gait speed and gait cycles), limiting the analysis and

outputs. Instead, these studies focus on imposing _external_ constraints (e.g., walking speed36,37 and spatiotemporal parameters3,38). This is also the case with publicly available

datasets9,10,39,40,41,42,43,44. Third, sex, age, and/or speed-matching, common in comparing gait across diverse cohorts37,45, is limited to group averages and trend comparisons.

Consequently, an understanding of person-specific adaptations coming from individual biomechanical features is lacking. Our study was designed to address these gaps and provide insights into

individual gait adaptations when walking with _internal_ constraints. A group of 21 neurotypical young adults (30 ± 8 years) was recruited for the study conducted in the CAREN biomechanics

lab (Motek Medical B.V., the Netherlands) at The University of Melbourne, Australia. Neurotypical participants were chosen instead of neurologically impaired for two reasons. First, the

demands of the study are too large for a patient group, and it would be unethical to impose further constraints on their restrained physical ability. Second, similar trends in neurotypical

and impaired gait24,25,33,34 provide evidence that the underlying neuromusculoskeletal mechanisms governing human walking do not get overridden but simply change their manifestation in the

presence of neurologically originated physical impairment. This suggests that insights gained from studying neurotypical populations could potentially be used to better understand patient

walking as well, provided a cautious and informed approach is taken in translating the findings. To allow the translation of the study findings into a better understanding of patient

walking, the study was designed in discussion with experienced physiotherapists from two clinics in Melbourne. As a result, participants walked at two speeds relevant to hemiparetic patients

(0.4 and 0.8 m/s) and one common in the neurotypical population (1.1 m/s). Preliminary tests showed that locking the knee joint using passive orthosis is most likely to simulate

_hemiparetic conditions_ and produce hip hiking (_‘unaffected frontal hip/pelvic angle when the affected limb is in mid-swing,’_46), circumduction (_‘greater-than-normal frontal thigh angle

during mid-swing of the affected leg,’_46), and step-to-walking (a gait where _‘toe of the affected side does not pass the toe of the non-affected side in the stance phase’_,47), all common

compensations in hemiparetic patients. This was demonstrated in our preliminary study in two participants48. The participants walked at five step frequencies (from − 10% to +20% of their

preferred cadence) per speed since changing step length and time impacts both neurotypical and patient walking. The protocol was spread across three days/sessions to minimise fatigue and

make the study technically and resource-wise feasible. Finally, to avoid problems of comparing data across different study designs, four different types of data relevant to characterise

human walking was collected: ground reaction forces through a dual-belt instrumented treadmill, joint movements using marker-based motion capture system, muscle activations using surface

electromyography, and metabolic cost using a portable indirect calorimetry device (see Fig. 1). In summary, the unique features of the presented dataset, publicly available on figshare49

include: * COMPREHENSIVE MEASUREMENT DATA – our dataset contains four different types of data that have commonly been used in characterising human walking: joint kinematics, ground reaction

forces, muscle activity, and metabolic cost data, a combination rarely available in other studies; * HUNDREDS OF STRIDES PER CONDITION – unlike many datasets that only contain several

strides per condition, our dataset contains 5-min worth of strides per condition, allowing analysis of gait parameter acclimatisation (and plateauing) over time as well50; * INTERNAL

CONSTRAINTS – in addition to studying the effects on gait of changing external constraints (cadence, walking speed), our dataset also imposes internal constraints (limited joint angle),

providing insights into _how_ (through recorded adaptations) and _why_ (through metabolic, energetic, and biomechanical variables) humans walk the way they do in tested

conditions/environments; and * INDIVIDUAL LEVEL ADAPTATIONS – unlike studies that speed/age/sex-match their control and intervention groups and are thus limited to trends and group averages,

our dataset allows studying individual adaptations across many different conditions. METHODS PARTICIPANTS Neurotypical adults were invited to participate in the study through a public

advertisement made available on the official website of the Human Robotics Research Group of The University of Melbourne, Australia. The advertisement described the study goal, protocol, and

duration and provided communication channels for those interested in knowing more and/or joining the study. The interested participants were interviewed and formally recruited if they met a

set of inclusion/exclusion criteria. Inclusion criteria were (i) neurotypical young persons with no known lower limb impairments, (ii) 18-60 years old, (iii) 1.55-1.90 m tall, and (iv)

weighing between 50-90 kg. Exclusion criteria were (i) significant trauma to the lower limbs and/or orthopedic procedures in the last six months, (ii) any form of diagnosed gait

abnormalities and/or walking problems, (iii) cognitive and/or communicative problems affecting the ability to comprehend or follow instructions, and (iv) any other pain conditions. Following

this, 21 neurotypical young adults (5 females, 30 ± 8 years, body mass 72.7 ± 12.3 kg, height 1.72 ± 0.09 m, BMI 24.5 ± 2.7 kg/m2, mean ± standard deviation) participated in the study. Of

those, 19 completed the entire protocol, and two dropped out after two out of three sessions (their data are also available with the manuscript). The anthropometric details of all the

participants are summarised in Table 1. Only one participant (Sub3) had previous experience walking with a lower limb orthosis, albeit in a different study. All participants were provided a

detailed plain language statement and signed the informed consent form that included a clause on agreeing to use pictures/videos in data dissemination. The ethics committee of The University

of Melbourne approved the study (2021-20623-13486-3). EXPERIMENTAL CONDITIONS The experimental study was designed around three multidimensional factors: walking speed, step frequency (i.e.,

cadence), and physical constraint. Walking speed came in three levels, including slow (_v_1 = 0.4 m/s), medium (_v_2 = 0.8 m/s), and normal (_v_3 = 1.1 m/s). Step frequency came in five

different levels, including preferred (_f_3), two lower (_f_1 = 0.9_f_3 and _f_2 = 0.95_f_3), and two higher (_f_4 = 1.1_f_3 and _f_5 = 1.2_f_3) levels. Finally, physical constraint came in

two levels, including a condition without restrictions on the lower limbs (_c_1) and one with a unilateral knee orthosis (_c_2, shown in Fig. 1). The orthosis was always worn on the left

side with the intention of locking the participants’ knee joint in full extension. Each participant underwent all 30 combinations of the three factors (3 × 5 × 2) spread over two data

collection sessions (see next section for details). Table 2 gives an overview of the naming convention used in the data analysis (and found in the dataset shared on figshare). The choice to

go with asymmetric step frequency above and below the preferred one was made after preliminary testing showed that 0.8_f_3 (i.e., − 20%) at slow speed leads to highly imbalanced and

potentially dangerous walking. As mentioned, all testing was done on a dual-belt instrumented treadmill in the Motek CAREN lab installed at the biomechanics lab at The University of

Melbourne. EXPERIMENTAL PROTOCOL The study was organised into _sessions_, _bouts_, and _conditions_ in the specified order of complexity. The participants were asked to come into the lab on

three different days, each corresponding to one session. The first session (_Ses1_) served as a preparation and familiarisation day, while the second (_Ses2_) and third (_Ses3_) sessions

were data collection days. The two data collection sessions were organised into 3 bouts each, with bouts differing in a unique _v__i_ − _c__j_ speed-constraint factor combination. A bout

consisted of 5 walking conditions executed at the same walking speed with no breaks in between. With that, each participant underwent 15 (half) walking conditions per session, reducing the

potential impact of fatigue while still collecting large amounts of data. The order of the cadence factor levels (i.e., _f_1 to _f_5) within a single _v__i_ − _c__j_ speed-constraint factor

combination (bout) was always randomised. On the other hand, the order and distribution across sessions of bouts were only _partially_ randomised to avoid familiarisation effects. For

example, multiple occurrences of the speed (e.g., _v_1 − _v_3 − _v_1) or consecutive occurrences of the impairment factor (e.g., _c_1 − _c_2 − _c_2) within a single session were rejected,

reducing the number of randomisation outcomes. This was further extended by the requirement to avoid the order-of-speed bias, leading to a uniquely determined choice of the speed-constraint

factor combinations available to choose from. Fig. 2 gives an example of the conditions randomisation for a single participant. PREPARATORY SESSION (SES1) The first session served as a study

familiarisation and baseline day. The participants started by walking at 1.1 m/s and preferred cadence for six minutes50 (Fig. 3, top - Part 1). The preferred cadence was estimated during

the last 15 seconds of that period using a simple script that counts heel strikes in real-time. This number was then used to guide two subsequent 2-minute exploration periods at the same

speed. During the first 30 seconds of each exploration period, a metronome guided participants 20% higher or lower from their previously estimated preferred cadence in a randomised order.

The participants were then allowed to go back to their preferred cadence for the remaining 90 seconds in each 2-minute period. The same was repeated at 0.4 and 0.8 m/s walking speeds in a

randomised order, albeit with a shorter, 3-minute familiarisation period (as opposed to a 6-minute period at 1.1 m/s). This allowed the calculation of preferred cadences based on three

experimentally measured values51. The preferred cadence at each speed, calculated as an average of the three measured values, is given in Table 3. The participants were then instructed to

find their preferred walking speed using the staircase method52,53. In short, the treadmill speed was twice increased from 0.5 m/s and decreased from 1.6 m/s in 0.1 m/s increments (every 10

seconds), until participants identified their preferred speed. The initially identified value was fine-tuned by changing the speed in 0.05 m/s increments up or down until the person settled

into the final value for that segment. This speed was noted 4x and averaged to obtain the final preferred speed. This was followed by a 7-minute walking test at that preferred speed, similar

to the tests at the other three speeds as described above, with the aim of finding the preferred cadence at the participant’s preferred speed. The process of finding each person’s preferred

step frequency (cadence) at each of the three speeds was subsequently repeated while wearing the orthosis (Fig. 3, top - Part 2). Similarly, the preferred cadence was estimated during the

last 15 seconds of three subsequent periods using a simple script. The main difference between the free (Part 1) and constrained (Part 2) walking parts lies in how a preferred speed is

found. Unlike the staircase method used during free walking, a simple increase in the treadmill speed until participants provided an auditory cue was used during constrained walking. This is

because constrained preferred speed is not used in data collection sessions, thus making finding its precise value less critical. The main reason for still finding the preferred speed

during constrained walking was to allow participants to familiarise themselves with the orthosis by giving them more time to walk with it. Once the self-selected speed was identified, which

usually took a couple of minutes, all participants were given 20 minutes of walking with the orthosis at the identified speed, split into two 10-minute walking periods at the start and end

of Part 2 (see Fig. 3). DATA COLLECTION SESSION (SES2/SES3) During the two data collection sessions, participants walked across three speed-constraint factor combinations (i.e., bouts), each

taking 25 minutes (Fig. 3, bottom). Without a break in between different cadences (1 cadence = 1 test = 5 min), each bout had speed and impairment fixed while sweeping through all five step

frequencies (_f_1 to _f_5) in a randomised order. A metronome guided participants to ensure the desired step frequency, a practice shown not to affect the metabolic cost (MC)51. The test

length of 5 minutes is based on a previous study that demonstrated that it takes about 3 minutes for participants to reach 95% of their metabolic steady-state3, leaving two minutes’ worth of

data for averaging (one in a more conservative case). The three bouts were separated by a 5-10 minute break and another 5 minutes to measure resting metabolic cost while standing. In total,

the resting MC was measured five times throughout each session to capture a potential increase in resting energetic demands as the session proceeds and thus validate the metabolic-level

fatigue effects. The effects of potentially accumulating fatigue were also measured using EMG amplitude signal (in addition to MC) during walking at a preferred speed, a 6-minute test

repeated at the start and end of each session. The former (i.e., muscle-level fatigue) can be validated using different methods in both time and frequency domains54. In terms of the

equipment, participants were fitted at the start of each data collection session with retroreflective markers that capture their lower limbs (and in some cases, upper body) movement in

space, wireless surface electromyography (EMG) to measure their EMG amplitude (an estimate of muscle activity), and a portable indirect calorimetry system to measure their energy use. An

experienced postdoctoral researcher, trained in placing the equipment and having previously led two human studies, put all sensors and markers on for all participants. The first 6-minute

walk at the preferred speed served as a warm-up trial50 and to control the potential fatigue effects. The test was immediately followed by ten repeated motions of sitting down and standing

up (about knee height chair), and two passes up and down a flight of 15 stairs. The muscle activity measured during these two tests and a 6-minute walking can be used in finding a muscle

activity normalisation factor55 (see later sections for more details). Participants were asked to comply with the following before coming into the lab: (i) no strenuous exercise at least 12

hours, preferably 24 hours before coming into the lab, (ii) no alcohol, caffeine, or nicotine in the period of 4 hours before the session start, and (iii) large meal no later than 1.5 hours

before coming into the lab. Participants were only allowed to drink water during the session, and no food was permitted. WALKING SPEEDS The _normal_ walking speed was initially set to 1.25

m/s as this speed is commonly used in tests with healthy participants56,57,58,59,60 and is considered the preferred average walking speed in young adults9,13. However, walking with the

orthosis at that speed and +20% step frequency proved to be potentially dangerous for the participants, so a decision was made to reduce this to 1.1 m/s. The other two speeds were chosen to

facilitate healthy-patients walking comparison, acknowledging that differences between the two groups go beyond simply speed-matching their gaits. The 0.8 m/s is considered a lower boundary

of the so-called community ambulator category61 and is typical of high-functioning hemiparetic patients25,29,34,62. The 0.4 m/s is considered an upper boundary of the so-called household

ambulator category61 and is typical of low-functioning hemiparetic patients25,63,64. UNILATERAL KNEE ORTHOSIS A simple, in-house designed orthosis/brace was used to lock the knee joint and

elicit compensatory movements during walking (Fig. 4a). The orthosis consists of two 3D-printed cuffs and two metal bars with a double-hinge joint connecting the cuffs. The cuffs are based

on a 3D model of several human legs obtained using a 3D scanner and imported into 3D modelling software. The cuffs come in several sizes to account for variations in the participants’ legs.

Furthermore, the design allows changing the cuffs’ relative position with respect to the knee joint to provide a better fit and comfort. For consistency, participants always wore the

orthosis on their left leg (Fig. 4b,c). To allow the placement of retroreflective markers for the motion capture system and EMG sensors, cuffs come with multiple cut-outs. The cuffs were

secured in place by four BOA straps in total, two on each cuff. A double-hinge system allows adjusting the orthosis to different leg sizes and changing the fixed knee angle. In this study,

the fixed knee angle was set by locking the hinge when a participant was standing with their knee fully extended, rendering a zero biological knee angle in a static pose. During experiments

(i.e., dynamic movements), the compliant tissue in human legs inevitably allows a small amount of knee flexion in the swing phase of gait. As a consequence, the effective knee flexion during

walking was always higher than zero (up to 20° in the swing in some participants). The orthotic movement did not cause any discomfort or pain to participants, likely due to the soft padding

lined on the cuff’s inner side. DATA COLLECTION AND PROCESSING Four categories of data were collected in the study: lower limb kinematics using a marker-based motion capture system (in the

case of half of the participants also upper limb, see Table 4), ground reaction forces (GRF) using a force-instrumented dual-belt treadmill, an estimate of muscle activation using wireless

surface electromyography (EMG) sensors, and the metabolic cost of walking using indirect calorimetry. All data processing was done using custom-written scripts in Matlab 2021a (Mathworks,

Massachusetts, USA). KINEMATICS AND FORCES DATA Gait data was collected in the Motek CAREN (Motek Medical B.V., the Netherlands) lab environment using a dual-belt instrumented treadmill and

Vicon motion capture system with 10 cameras (Vicon Motion Systems, Oxford, UK). The marker data was collected at 100 Hz, and force data (GRF) at 1 kHz. Participants were fitted with a custom

set of 26 retroreflective markers (spherical, 14 mm diameter, B&L Engineering, CA, USA) bilaterally placed in the pelvic area and on lower limbs. Of the 26 markers used, 23 are

functional following65, and the remaining three are added to allow differentiating the two legs through asymmetric marker patterns. Half the participants also agreed to have markers placed

on their torso and arms, in which case a custom set of 11 markers was used (Fig. 5a,b). It should be noted that while 26 lower limb and 11 upper limb markers were the default marker

configuration, some participants had 24 lower limb markers and 9 upper limb markers, with two auxiliary markers missing in both cases. This was the case with a few early participants and is

indicated in Table 4. The two missing markers include _LTIBaux2_ and _RTIBaux2_ in the case of lower and _RArmWristAux_ in the case of the upper limb. Data were segmented into steps using a

5% GRF signal threshold (i.e., for a 70 kg person, the threshold is 70 ⋅ 9.81 ⋅ 0.05 = 34_N_), as this was the lowest threshold across all participants that had a negligible amount of false

positives. False positives occur when the force oscillates around the defined threshold, resulting in, e.g., multiple heel strikes detected in a single step. The provided gait segmentation

algorithm comes with a code for the automated correction of false positives. The code also corrects for the missing steps, which occur when a person steps on the opposite belt (i.e., left

leg on the right belt, called overstepping). Segmented and corrected data were subsequently time-normalised to 0-100% using linear interpolation, with the step defined as heel strike to

subsequent heel strike. The number of detected steps on each side (and consequently, the number of heel-strike and toe-off events) is not necessarily equal in the raw data and was thus

corrected in post-processing for the simplicity of the subsequent analysis. Furthermore, the steps were removed if/where needed at the start/end of a dataset (i.e., a 5-minute test) to

ensure every dataset starts with the left and ends with the right step (again, for the analysis’ simplicity). The marker data were filtered using a 4th-order zero-lag Butterworth filter with

a 6 Hz cut-off frequency9. Filtered marker coordinates in 3D space of lower limbs were used to calculate joint angles (ankle, knee, hip, and pelvis) in the sagittal plane as per Research

Methods in Biomechanics66 and following ISB (International Society of Biomechanics) guidelines67. Torso markers are only available for half the participants but do not allow for a similar

calculation of the upper limb joint angles – their purpose was to capture arm swing movements and chest movements rather than upper limb joint angles. The chest movement is particularly

useful in calculating, e.g., Lyapunov stability index52,68, an increasingly relevant index in the patient population. A standing static calibration trial was performed at the start of each

session to build a marker template and define _offset_ joint angles. These angles capture imperfections in placing markers (e.g., a measured 1° knee flexion despite the person standing with

their knees fully extended) and were subtracted from dynamic movement to obtain a true measurement of the joint angle (i.e., walking ROM) rather than errors in marker placement. During the

static trial, participants were instructed to stand still, hip-width apart with straight knees and back and feet parallel with toes facing forward, as seen in Fig. 5c66. Study investigators

demonstrated this position to each participant and ensured they followed the instructions. Once calculated for the entire test, kinematic data were segmented into gait cycles using heel

strike and toe-off event indices determined using GRF signal. EMG AMPLITUDE DATA The EMG amplitude was measured in eight muscles per leg using surface electromyography (sEMG) Delsys Trigno

(Delsys Inc., Natick, MA, USA) system. The raw data signal, captured at 2 kHz, was read by the Vicon PC running Nexus 2.8 via cable connection for data synchronisation purposes. For

participants S1-12, the connection was done using a USB cable (trigger not available), which Delsys claims results in a synchronisation error of ± 20 ms on average (in our experience, the

variations can significantly exceed this estimate). For participants S13-21, a trigger was available to ensure no-delay synchronisation. NOTE: The delay was corrected in participants S1-12

in post-processing by cross-correlating accelerometer data from the Right Tibialis Anterior EMG/IMU sensor with accelerations from RTibExt marker data, a method validated on the synchronised

data from participants S13-21. The correction (in ms) for each participant is given in Supplementary File, Table S1. A validation of this approach for correcting delay is demonstrated in

Supplementary File, Fig. S1. The wireless Trigno Avanti sensors were placed in line with muscle fibers69 over the Tibialis Anterior (TibAnt), Gastrocnemius Lateralis (GastroLat) and Medialis

(GastroMed), Vastus Lateralis (VastLat) and Rectus Femoris (RecFem), Biceps Femoris (BicFem) and Semitendinosus (Semitend), and Gluteus Maximus (GlutMax) muscles (Fig. 4a,b). The same order

of muscles is preserved in raw files provided with the dataset, with muscles 1-8 (EMG sensors 1-8) corresponding to the left leg and 9-16 (EMG sensors 9-16) to the right leg. NOTE: Data are

organised per muscle name and not per sensor name.) Each participant’s skin was prepared following the SENIAM guidelines (website, 2023). This included shaving the hair in the spots of the

sensor placement, as well as cleaning the skin with alcohol and ensuring it was dry before the sensors were attached. The processing of the raw EMG data started by detecting a linear

envelope. This was done using a band-pass filter (10-500 Hz, implemented on the Trigno sensors) followed by a full-wave rectification and smoothening (a 100 ms moving average window).

Linear-envelope data were normalised using pooled EMG activity collected during stair climbing, sit-to-stand activity, and baseline walking test at the start of each session. Maximal muscle

activity was determined in each muscle per person per activity, and the highest value across the three tests per muscle was used as a normalisation factor (see Table S2 in Supplementary

File). The reason for not using Maximal Voluntary Isometric Contraction (MVIC) tests was the lack of necessary equipment to correctly measure maximum EMG amplitude and to avoid major

drawbacks of the isometric contraction approaches55,70, including inconsistent maximal contractions and inability to volitionally contract specific muscles. The activity-based method used

herein also has the benefit of ensuring normalised muscle activity to be between 0 and 1 (muscles are generally more active in stair climbing and sit-to-stand movement than walking), which

is a prerequisite for using these data in driving musculoskeletal models (e.g., OpenSim). Other ramifications of this approach can be found in the section Usage Notes. Muscle activity data

were segmented into gait cycles using heel strike and toe-off event indices determined using GRF signal. Additionally, a signal-to-noise (SNR) ratio, calculated as 20 ⋅ 

_l__o__g_10(_s__i__g__n__a__l__r__m__s_/_n__o__i__s__e__r__m__s_), is provided for each muscle across all gait cycles (similar to segmented EMG amplitude). Observed noise levels were

predominantly in the ( − 5, 5)_μ__V_ and ( − 3, 3)_μ__V__r__m__s_ range, so the latter is used in the provided SNR calculations. Whenever SNR > 6 dB, which corresponds to the RMS

amplitude of the actual EMG signal that is more than twice the RMS amplitude of the noise, the signal is considered good. NOTE: Normalisation factors of each activity (walking, stair

climbing, and sit-to-stand) can be extracted and used separately from the provided raw EMG data of walking, stair climbing, and sit-to-stand activity. METABOLIC DATA Respiratory _O_2

consumption and _C__O_2 production were collected using PNOE indirect calorimetry system (Endo Medical, Palo Alto, US), consisting of an appropriate face mask and a portable measurement unit

worn on the back. The masks come in different sizes to accommodate different participants and minimise the exchanged gas leakage. The data of the last minute of both 5-minute resting

(standing still) and 5-minute walking tests were used to calculate the energy consumption using the simplified Brockway formula71. Net MC of walking (in _W_/_k__g_) was then calculated by

subtracting the resting value measured before each bout from all 5-minute tests within that bout and dividing by the participant’s mass. This was done to avoid the effect of potentially

accumulating fatigue reflecting in the net energetic cost of the tests later in the session. Furthermore, the metabolic cost of transport (in _J_/_k__g__m_) was calculated by dividing the

normalised MC by the walking speed. NOTE: Resting MC throughout each session is provided with the dataset. DATA RECORDS The full dataset is available on figshare49. Since figshare repository

does not allow folder structure at the time of submission, all data are uploaded as .zip files, grouped according to the content organisation depicted in (Fig. 6 and Table 5). More details

are provided in the subsequent sections. Where possible, the same naming and data structure conventions are maintained across different data groups for consistency. RAW DATA CONTAINS: ground

reaction force (GRF) and the feet centre of pressure (CoP) data; the 3D position of all the markers; muscle activity (EMG) data for each of the sixteen muscles; and metabolic cost (MC) data

as exported by the metabolic analyser. DESCRIPTION: Raw data are stored in .csv, .c3d, and .mat formats, all widely used and accepted file formats. The CSV format is a text format that can

be read by any text file reader, including Microsoft Excel and Matlab. This is arguably the format that will reach the largest user base. The C3D format is typically used in the biomechanics

community, but since it’s a binary format, it requires extra steps and processing before it can be accessed. Finally, raw data are also provided in MAT format that can be easily read in

Matlab and Python, the two most commonly used programming languages in the research community. Where necessary, raw data files in CSV and MAT formats were _pre-processed_ to, e.g., remove

data from a 5-min test if the participant had to stop to fix a marker or reduce GRF frequency from 2 kHz to 1 kHz (an artifact of the originally collected GRF data frequency). The _RawData_

on Level 1 are organised into three groups: _CSV_, _C3D_ and _MAT_, corresponding to the format the data are saved in. Within each group, data are separated per participant on Level 2,

resulting in 21 sub-groups in each _CSV_, _C3D_, and _MAT_ group. The data files available for download from figshare reside on Level 3 as .zip files. When downloaded and opened, the fields

from Levels 4-7 will become available for further analysis and manipulation. In particular: * .CSV – files available for download are called, e.g., _RAW_csv_Sub17.zip_. Within this file,

data are separated into four folders, corresponding to _Kinematics_, _Kinetics_, _EMG_, and _MC_. The _Kinematics_ folder contains one CSV static calibration file per session, CSV files of

all 30 tests (T1-T30), and two CSV files per session of preferred walking (at the start and end). Similar files can be found in the _Kinetics_ and _EMG_ folders, with the addition of CSV

files for stair negotiation and sit-to-stand in the latter. The _MC_ folder, on the other hand, contains three CSV files per session of 25-minute bouts, five CSV files per session of

resting, and two CSV files per session of preferred walking. The file naming convention concatenates participant identification, type of data, test number, and session number where

applicable. For example, marker data of S6 in T16 is located in _Kinematics_ folder under name _Sub6_Kinematics_T16.csv_ and will become available once the _RAW_csv_Sub6.zip_ is opened.

Similarly, MC data of S6 in Bout2 of Session2 is located in _MC_ folder under name _Sub6_MC_Bout2_Session2.csv_. * .C3D – files available for download are called, e.g., _RAW_c3d_Sub5.zip_.

Since data are saved into a single C3D file per test, the names within the zip file are concatenated subject identification (e.g., Sub5) and test name (e.g., T17). For example, the raw data

of S5 and test T17 is called _Sub5_T17.c3d_ and will become available once the _RAW_c3d_Sub5.zip_ file is opened. Apart from the C3D files of all 30 tests (T1-T30), each subject-specific

folder also contains C3D files of static calibration (one per session) and preferred walking trials (two per session, one at the start and one at the end). * .MAT – files available for

download are called, e.g., _RAW_mat_Sub13.zip_. To avoid too large file sizes, GRF, CoP, and markers data are grouped into _Mechanics_, and EMG and breath data into _Energetics_. The

subsequent level in the tree structure is test names, followed by one of the data groups. For example, raw GRF data of S4 in test T16 is saved in _Sub4_rawMechanics.mat_, which will become

available once _RAW_mat_Sub4.zip_ is opened. Similarly, raw markers data of S20 in test T5 is saved in _Sub20_rawMechanics.mat_ under the _rawMechanics.T5.Markers_ field, which will become

available once _RAW_mat_Sub20.zip_ is opened. Marker positions during a static calibration trial for the same subject can be found under _rawMechanics.StaticCalibration_ field in the same

MAT file (separated into Session 2 and Session 3 data). Similarly, raw EMG data of S9 in T22 is saved in _Sub9_rawEnergetics.mat_ under the _rawEnergetics.T22.EMG_ field, which will become

available once _RAW_mat_Sub9.zip_ is opened. In addition to static calibration and data from 30 walking tests, each MAT file also contains raw data from the two walking tests at the

preferred speed, divided into two sessions. SEGMENTED DATA CONTAINS: ground reaction force (GRF) and the feet centre of pressure (CoP) data; calculated joint and pelvic angles in all three

degrees of freedom (DoFs); muscle activity data; calculated metabolic cost data (from breaths); heel-strike and toe-off events used in gait segmentation; and EMG normalisation factors

including activities (e.g., walking) they were obtained from. DESCRIPTION: Segmented data are saved in .mat format and can be accessed by opening _SEGMENTED_mat_SubX.zip_ files. Similar to

raw MAT files in _RAW_mat_SubX.zip_, all data is grouped per subject, resulting in 21 subject-specific groups. GRF, CoP, joint and pelvic angles, and heel-strike and toe-off events are

grouped into _Mechanics_, while EMG data, EMG normalisation factors, and calculated metabolic cost are saved under _Energetics_. For example, segmented joint angles data of participant S19

in test T30 is saved in _Sub19_segMechanics.mat_ under the _segMechanics.T30.Angles_ field, while segmented EMG data of participant S8 in T11 is saved in _Sub8_segEnergetics.mat_ under

_segEnergetics.T11.EMG_ field. Both .mat files will become available once relevant _SEGMENTED_mat_SubX.zip_ files are opened. EMG normalisation factors for the same participant (i.e., S8)

can be found under _segEnergetics.NormFactors_ field in the same MAT file (separated into Session 2 and Session 3 data). As mentioned earlier, data is segmented from the left heel-strike to

the subsequent left heel-strike event, and time normalised to 101 data points. Data for each variable (e.g., hip joint angle, vertical GRF) are saved in matrices such that gait cycles always

correspond to rows, and gait percentage to columns (1% of gait = 1 data point = 1 column). Except for pelvic angles and metabolic cost data, which are side-independent, all data is

separated into the left and right sides. Furthermore, metabolic cost data is calculated on a breath-by-breath basis but is added to the segmented data folder as that seems appropriate.

MISSING DATA The PNOE device used to measure _O_2 consumption and _C__O_2 production malfunctioned in several sessions, resulting in partial data loss in the case of five subjects. These

include participants S5, S6, S8, S11, and S12. An overview of the metabolic cost data across all participants and conditions (5-min tests) is given in Table 6. A wrong setting was used with

the Delsys proprietary data collection software (EMGWorks) when collecting muscle activity data during stair negotiation at the beginning of Sessions 2 and 3. For this reason, and only for

participants S13-S21, EMG data is missing for the right Semitendinosus muscle in stair climbing. Consequently, this muscle’s activity was set to zero when using it in an activity-based

normalisation process (i.e., column 15 in a 16-column EMG matrix is zero). Further to issues with EMG data, the sensor measuring left Tibialis Anterior malfunctioned in Session 3 for

participant S15. To keep the symmetry, some sensors were reshuffled, including moving the sensor from the left Gluteus Maximus to the left Tibialis Anterior and not measuring the former, as

well as removing the sensors from the right Gluteus Maximus. In raw data files, this simply means that both left and right Gluteus Maximus are zero in all tests for participant S15 in

Session 3. Note, however, that data for Left Tibialis Anterior is saved in raw files in column 8 in a 16-column EMG file, corresponding to Left Gluteus Maximus. TECHNICAL VALIDATION The

collected data was validated to confirm consistency within and across participants, as well as in comparison to available datasets, providing confidence in its physiological accuracy and

relevance. Representative trajectories are visualised by taking different participant’s data from a single session, as follows: ground reaction forces of S2 in Fig. 7; joint kinematics data

of S8 in Fig. 8; metabolic activity of S17 in Fig. 9, and electrical muscle activity of S1 in Fig. 10. Further dataset validation and a demonstration of the sort of analyses enabled by our

dataset is provided by comparing joint kinematics, GRF, and EMG data from S13 across the two sessions (during a 6-minute _PrefStart_ walking trial) in Fig. 11 and by visualising temporal

adaptations during a single 5-minute test from S15 in Fig. 12. All data are segmented between the subsequent heel strikes of the same leg; for visualisation purposes, GRF data of both legs

are plotted between left heel strikes; on the other hand, joint angles and muscle activity are plotted between ipsilateral heel strikes (left side between left, and right side between right

heel strikes). All trajectories are averaged over the last 20% of cycles (approximately the last minute of walking in a 5-minute test), and data are taken from the tests with the preferred

cadence. To assess the validity of the presented dataset, a comparative analysis is provided with two publicly available datasets that have similar walking speed conditions as our dataset.

The first publicly available dataset used in the comparison is Bovi _et al_.10, which presents a multi-task analysis of human walking (and stair negotiation) in 20 healthy adults whose age,

height, and weight distributions include the ones found in our sample (in the Bovi dataset, the participants were 43 ± 15 years of age, with average body mass 68.5 ± 15.8 kg and height 1.71 

± 0.1 m). The comparison is limited, however, due to the differences in the ways data were collected. Bovi _et al_. collected their data by asking participants to walk over a walkway as

opposed to a treadmill, which limits their number of gait cycles and does not account for familiarisation taking place when walking over longer periods. Furthermore, the participants in Bovi

_et al_. first walked at their preferred speed, which was then progressively increased and decreased without any precise indications of the desired gait speed. During the analysis, walking

speeds were _a-posteriori_ categorised into _very slow_, _slow_, _medium_, and _fast_ walking defined by the ratio of the freely chosen speed and participant’s height. To allow comparison

between the two datasets, we multiplied the average participant’s height by the average gait speed in each of the four categories and took data from two speed categories that most closely

resemble gait speeds in our study. These included 1.22 m/s and 0.84 m/s, which are compared to our 1.1 m/s and 0.8 m/s trials (see Fig. 13). The second publicly available dataset used in the

comparison is van der Zee _et al_.72, which presents data of ten healthy adults walking at a range of speeds (0.7-2.0 m/s) in a lab. With a mean age of 24 ± 3 years, an average body mass of

73.5 ± 15 kg, and an average height of 1.76 ± 0.11 m, the participants in van der Zee _et al_. study are similar in size albeit a bit younger compared to participants in our study.

Different from comparing our dataset to the Bovi _et al_. dataset, though, the comparison to van der Zee _et al_. dataset is more direct due to several similarities in data collection. These

include subjects walking on a dual-belt instrumented treadmill at their preferred gait and 1.1 m/s. For this reason, it was possible to compare speed-matched joint kinematics and GRF, which

are given in Fig. 14. A notable difference between the two datasets are shorter tests in the van der Zee _et al_. dataset, where participants were asked to walk only 60 seconds per

condition compared to 300 seconds in our dataset. USAGE NOTES During data collection and exporting from the Vicon Nexus software, we’ve encountered several issues that are reflected in the

provided datasets. To avoid these interfering with data analysis and explain occasional unexpected results, a summary of the kinematics and kinetics data features and issues is summarised in

Supplementary File, Table S3 and ground reaction force issues in Table S4. Generally, the issues can be separated into test-related and export-related. The test-related issues refer to

instances when a test had to be briefly stopped due to, e.g., a loose marker, which resulted in a short period of unusable data (the test recording was not stopped when such interruption

happened). On the other hand, export-related issues refer to an atypical sampling frequency of the data exported from Nexus, such as ground reaction forces exported at 2 kHz instead of 1

kHz. The corrections made to the data, such as removing unusable data of a person standing on the treadmill due to marker fixture or downsampling GRF data to 1 kHz, are only done in the MAT

files. The C3D files contain the raw data as it was collected/exported, with no corrections whatsoever. LIMITATIONS The study is designed with a focus on individual adaptations rather than

trends or averages across groups. This is seen in the same group of participants being both the _control_ and the _intervention_ group, resulting in 30 different conditions for each

participant, sufficient to investigate individual adaptations. At the same time, a small sample size (N=21) requires a cautious approach when the focus shifts away from individuals, and data

are used to find trends and averages in the sample and apply the findings to the population the sample is taken from. Further to the sample characteristics, the study recruited

predominantly male participants despite the efforts to make the gender distribution as equal as possible. Studies that analyse human gait in a controlled lab environment use both overground

and treadmill walking protocols, albeit more often the former13. Despite some researchers calling for a cautious translation of results between the two, no clear and uniform differences seem

to exist between the two protocols40. Where our treadmill-based dataset does fall short is in addressing spontaneous changes in walking speed as documented by Ojeda _et al_.73. Finally, due

to several issues with the PNOE metabolic cost analyser we experienced with some of the subjects, the metabolic cost measurement is missing in 36 out of 330 5-minute tests collected (about

11%). However, statistical and computational modelling tools exist in the literature that can be used to estimate the missing test values using the available MC test values. Notably, some

statistical analyses can handle the amount of missing data in our dataset without the loss of statistical power. The EMG amplitude is normalised to the peak EMG signal measured during three

everyday activities – walking, sit-to-stand, and stair climbing. As highlighted by Besomi _et al_.70, this approach limits the normalised data analysis to the following contexts: * amplitude

comparison within a person and muscle, within a session; * amplitude comparison within a person and muscle, across sessions (with caution); * comparison of shape/activation patterns (timing

of bursts, timing of peaks, period of inactivity). A direct amplitude comparison between muscles is not recommended with this EMG amplitude normalisation method since the comparison is done

to the muscles peak activation within a given activity and not its maximum contractive capacity. CODE AVAILABILITY A file containing multiple Matlab® scripts is available on figshare49 as a

separate .zip file with the suffix _Other_. These scripts allow simple analysis and visualisation of ground reaction forces and joint angles (_MAIN_KineticsKinematics.m_), metabolic cost

and cost of transport (_MAIN_MetabolicCost.m_), and muscle activation (_MAIN_Emg.m_). The same code can also be found on the project’s GitHub website, accompanied by the instructions on how

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authors would like to thank Prof. Gavin Williams of the Epworth Hospital and the University of Melbourne, Prof. Jennifer McGinley of the Physiotherapy Department at the University of

Melbourne, and Dr. Liuhua Peng, lecturer of the School of Mathematics and Statistics with the University of Melbourne for their expertise in designing the study. The authors would also like

to express sincere gratitude to the 21 participants who volunteered their time and effort to participate in our study. Their contributions were invaluable and will be crucial in bettering

our understanding of the underlying walking mechanisms. The authors thank them for their patience and cooperation throughout the study. Without their participation, this research would not

have been possible. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * The University of Melbourne, Department of Mechanical Engineering, 3010, Melbourne, Australia Tomislav Baček, Mingrui Sun, 

Hengchang Liu, Denny Oetomo & Ying Tan * Monash University, Faculty of Engineering, 3800, Melbourne, Australia Zhongxiang Chen & Dana Kulić * The University of Melbourne, Department

of Electrical and Electronic Engineering, 3010, Melbourne, Australia Chris Manzie * Imperial College London, Department of Bioengineering, London, United Kingdom Etienne Burdet Authors *

Tomislav Baček View author publications You can also search for this author inPubMed Google Scholar * Mingrui Sun View author publications You can also search for this author inPubMed Google

Scholar * Hengchang Liu View author publications You can also search for this author inPubMed Google Scholar * Zhongxiang Chen View author publications You can also search for this author

inPubMed Google Scholar * Chris Manzie View author publications You can also search for this author inPubMed Google Scholar * Etienne Burdet View author publications You can also search for

this author inPubMed Google Scholar * Dana Kulić View author publications You can also search for this author inPubMed Google Scholar * Denny Oetomo View author publications You can also

search for this author inPubMed Google Scholar * Ying Tan View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS T.B. initiated, ideated,

prepared, and led the experimental study and data collection, wrote data analysis code, exported and analysed data, drafted all manuscript versions, and prepared figures, tables, and data

visualisation. M.S., H.L., and Z.C. participated in designing and carrying out the study protocol and provided feedback on the manuscript. C.M. and E.B. contributed to the design of the

project concept, project supervision, and provided feedback on the manuscript. D.K., D.O., and Y.T. provided feedback on the manuscript and contributed to developing methodology, data

analysis and interpretation, project administration and supervision, and secured funding for the project. CORRESPONDING AUTHOR Correspondence to Tomislav Baček. ETHICS DECLARATIONS COMPETING

INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and

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http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Baček, T., Sun, M., Liu, H. _et al._ A biomechanics and energetics dataset of

neurotypical adults walking with and without kinematic constraints. _Sci Data_ 11, 646 (2024). https://doi.org/10.1038/s41597-024-03444-4 Download citation * Received: 04 May 2023 *

Accepted: 30 May 2024 * Published: 18 June 2024 * DOI: https://doi.org/10.1038/s41597-024-03444-4 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this

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