A database of physical therapy exercises with variability of execution collected by wearable sensors

ABSTRACT This document introduces the PHYTMO database, which contains data from physical therapies recorded with inertial sensors, including information from an optical reference system.

PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations were recorded. The volunteers performed two series with

a minimum of 8 repetitions in each one. PHYTMO includes magneto-inertial data, together with a highly accurate location and orientation in the 3D space provided by the optical system. The

files were stored in CSV format to ensure its usability. The aim of this dataset is the availability of data for two main purposes: the analysis of techniques for the identification and

evaluation of exercises using inertial sensors and the validation of inertial sensor-based algorithms for human motion monitoring. Furthermore, the database stores enough data to apply

Machine Learning-based algorithms. The participants’ age range is large enough to establish age-based metrics for the exercises evaluation or the study of differences in motions between

different groups. Measurement(s) Turn rate • Specific force • Magnetic field • Locations • Orientations Technology Type(s) Inertial measurement unit • Stereophotogrammetric system Factor

Type(s) Time Sample Characteristic - Organism Humans SIMILAR CONTENT BEING VIEWED BY OTHERS DATABASE OF LOWER LIMB KINEMATICS AND ELECTROMYOGRAPHY DURING GAIT-RELATED ACTIVITIES IN

ABLE-BODIED SUBJECTS Article Open access 14 July 2023 A DATASET OF ASYMPTOMATIC HUMAN GAIT AND MOVEMENTS OBTAINED FROM MARKERS, IMUS, INSOLES AND FORCE PLATES Article Open access 30 March

2023 A HUMAN LOWER-LIMB BIOMECHANICS AND WEARABLE SENSORS DATASET DURING CYCLIC AND NON-CYCLIC ACTIVITIES Article Open access 21 December 2023 BACKGROUND & SUMMARY Home health care is

becoming more and more necessary, and not only for patients that have to follow a particular therapy after a surgical procedure, but also for people that require long-term therapies for a

comprehensive strengthening, as happens with older adults. This is one of the pillars of active aging, where a set of exercises is prescribed to slow down the effects of frailty1 and improve

patients’ functional ability2. Patients achieve the benefits of performing these therapies only when they remain consistent3. There is evidence that regular follow-up by health-care

professionals keep patients active over the long term4. However, it is difficult to have a carer at the patient’s disposal. This explains why virtual coaches are increasingly needed as an

alternative to health staff, that could contribute to patients’ adherence to physical exercise-based treatments5. In the absence of a personal supervisor, the system that works as a virtual

coach has to perform three main tasks. Firstly, it has to get information about human motion, based on sensory systems, such as optical or inertial ones, among others. Secondly, it has to

monitor physical activity and evaluate the performance of the exercises6,7, what requires the development of particular algorithms adapted to physical therapy exercises8,9,10, some of them

based on Machine Learning (ML) methods10,11, which relate parameters of human motions with the performance. And finally, the virtual coach has to provide feedback to the patient5. For the

evaluation and optimization of algorithms, it is necessary particular datasets adapted to the exercises to be analysed. In this way, recent research efforts have focused on the creation of

datasets by using different sensory systems. Video-based and portable technologies are the main alternatives in the monitoring of human activities12, so databases obtained with both systems

are of great interest. However, video-based technologies entail occlusions and patients’ privacy concerns13,14. Conversely, wearable systems, such as inertial measurement units (IMUs), are

becoming increasingly popular because of its practicality and its everywhere usable potential15. IMU-based databases of human motion monitoring commonly focus on the study and assessment of

gait16 or the study of activities of daily living17, and they usually also include depth and RGB sensors. This data descriptor aims to contribute to the research of the monitoring and

evaluation of the performance of prescribed physical therapy exercises through inertial wearable sensors. This database, called PHYTMO (from PHYsical Therapy MOnitoring) is created for its

use in the development of novel algorithms for the evaluation of human motions, including the identification and assessment of a known set of prescribed exercises. The database includes

enough data for developing ML-based algorithms, as the authors proved in their proposal for the exercises recognition and evaluation that uses part of the inertial data of PHYTMO18. For the

development of robust and generalizable algorithms, it is required a large amount of annotated data and the subjects variability is also important. PHYTMO includes data of the performance of

6 exercises and 3 gait variations commonly prescribed in physical therapies. Data are recorded with four IMUs placed on arms or legs, according to the performed exercise. Data also include

the position and orientation of IMUs in the 3D-space during the performance of the 6 exercises measured with an optical system. Data of each exercise are divided into two kind of series,

which consist in correctly and wrongly performed exercises. A total amount of 30 volunteers with variability in age and morphology performed these exercises, what makes it possible to study

the differences between kinematic parameters that can occur in the execution of exercises at different ages. The anonymous subjects can be easily associated with their anthropometric

information for this purpose. Furthermore, the data are labeled for the identification of each exercise separately, annotating its correct or incorrect performance. Furthermore, this

database can be very useful to train algorithms developed for human kinetic analysis. Using these data, IMU-based algorithms for kinetic parameter estimation can be developed or analyzed, as

different proposals found in the literature19,20,21,22. This application is especially remarkable because PHYTMO include data from IMUs together with reference data from an accurate optical

system, related to the 6 exercises based on repetitions of motions. Therefore, this database can be used to check different proposals using the same data, facilitating a fair comparison

between algorithms. METHODS PARTICIPANTS AND ETHICAL REQUIREMENTS Thirty volunteers enrolled in the study:13 women and 17 men. Table 1 shows their anthropometric information together with

their age, sex (masculine, M, or feminine, F) and their identifier (Id) in the database. Volunteers are separated by their age range in order to ease the analysis of different aged

population. These ranges are clustered by decade, so range A includes volunteers aged between 20 and 29 years old, B between 30 and 39, C between 40 and 49, D between 50 and 59 and E between

60 and 69. Table 1 also includes the volunteers’ motor conditions that may have influenced in their movements. All volunteers were healthy and robust and only three of them reported pain

during the motion, although they were able to perform all the evaluated exercises. The study was carried out in the framework of FrailCheck project (SBPLY/17/180501/000392), following all

the COVID-19 guidelines and recommendations. Volunteers wore masks during the exercises performance, which should be taken into consideration in the motion analysis because of possible early

fatigue even in healthy volunteers. Guadalajara University Hospital approved the study protocol (Institutional Review Board No.2018.22.PR, protocol version V.1. dated 21/12/2020), and all

participants signed a written informed consent. ACQUISITION SETUP The PHYTMO data set includes data recorded with four IMUs and an accurate optical system. These data were recorded in the

Motion Capture Laboratory of the University of Alcala using the _NGIMU_23 IMUs (_X-io Technology_, Bristol, UK) and the _OptiTrack_24 system (NaturalPoint Inc). Wearable sensors include

3-axis gyroscope, accelerometer and magnetometer, with a range of 2000°/s, 16 g and 1,300 _μ_T, respectively. These are common IMUs range values used for human motion monitoring, such as the

popular _XSENS_ sensors16. The sample rate was set to 100 Hz for the gyroscopes and accelerometers and to 20 Hz for the magnetometers in the reported data. The IMUs stored the recorded data

on an SD card and as each volunteer finished performing the designed set of exercises, we downloaded the data to the computer for further data processing. They have a size of 56 × 39 × 18 

mm with a weight of 46 g, what makes them practical for wearing during the performance of exercises. Each IMU was mounted on an _ad-hoc_ structure (mounting board) for its placing at the

limbs. Regarding the optical system, it is based on infrared light-emitting cameras situated in the capture room that identify the position of reflective markers placed on the subject

anatomic landmarks and on the IMU structure. The optical system recorded the motions of the volunteers and the devices they wore along the data collection. We used the _OptiTrack_ system,

which consisted of eight depth _Prime 13_ cameras with a resolution of 1.3 MP and a frame rate of 240 fps. We used this system with the _Motive 2.2.0_ software to calibrate it before each

use, to set the cameras rate to 100 Hz and to define the skeletons and objects corresponding to the IMU mounting boards to be recorded. ACQUISITION PROTOCOL The volunteer recordings were

made in a continuous session on the day each volunteer was available and the individual sessions lasted an average of two hours. At the beginning of each day recordings, we set the

coordinates origin of the optical system on the floor and always in the same point, so optical measurements are always referred to the same origin. We established the initial orientation of

the IMU mounting board in the optical system placing this board on the floor, so in this orientation the rotation angles are equal to zero. The IMU Y_I_-axis was parallel to the mounting

board Z-axis and the IMU Z_I_-axis was parallel to the board Y-axis, so the IMU X_I_-axis was anti-parallel to the board X-axis, as depicted in Fig. 1-left. The four IMUs were placed at the

upper-or lower-limbs, according to the performed exercises, with their X_I_-axis pointing to the ceiling, as the reference systems shown in Fig. 1-right. On the volunteers’ lower-limbs, the

IMUs were placed on the anterior surface, so when volunteers were standing, the Z_I_-axis was perpendicular to the coronal plane of their bodies and the Y_I_-axis was perpendicular to their

sagittal plane (Fig. 1-right depicts these positions). On their upper-limbs, IMUs were placed on the exterior lateral position. In this case, when the volunteers’ hands pointed to the floor,

the Y_I_-axis was perpendicular to the volunteers’ coronal plane and the Z_I_-axis was perpendicular to their sagittal plane. We chose these locations on the body for the easiness of

placing sensors. Each IMU has an identifier with format _Xsegment_, where _X_ refers to its position, left (“L”) or right (“R”), being _segment_ “thigh” or “shin”, referring to the segment

of the lower-limb the IMU was placed on, or “arm” or “forearm” for the upper-limb. We synchronized the four IMUs through the identification of significant events in specific motions at the

beginning of each recording. For the synchronization of the leg exercises, volunteers performed the three motions depicted in Fig. 2, which consisted in: keeping the leg straight, two hip

flex-extensions with the right leg to synchronize the two IMUs on this leg (Rshin and Rthigh), two hip flex-extensions with the left leg to synchronize the other two IMUs (Lshin and Lthigh),

and two knee bending with both feet together in order to synchronize both legs by the detection of peaks in the signals of turn rate recorded by Lshin and Rshin. Data of arm exercises

included two repetitions of straight arms elevation maintaining both hands together during the motion. After the synchronization phase, volunteers performed the different exercises or gait

variations, commonly prescribed in a specific way in physical therapies. These particular movements have been chosen as part of a physical exercise routine prescribed for elderly people to

maintain their functional capacity25. These exercises have been tested in some studies26,27, which demonstrate functional improvement in older adults after continued performance of them over

a relatively short period of time (approximately 3 months). The exercises can be divided into two groups of three exercises: focused on the lower-limbs and on the upper-limbs. Table 2 lists

all the exercises and gait variations carried out by the volunteers, and describes the correct way to perform them. Before the motion recording, an initial description of the exercises set

was explained by one of the authors of the study, who also performed each motion as demonstration. Besides the correct performance of the exercises, volunteers made them wrongly, receiving

no instructions in this case. However, most of volunteers performed the wrong repetitions of the exercises in similar way, so we also report the most common deviations during the wrong

performances in Table 2. The quality of the performance was evaluated by an expert person, who indicated how the proper exercises had to be done and labeled the exercises as correctly or

wrongly performed. We only consider one kind of wrong gait for the three variations (GAT, GIS and GHT) during which volunteers walk freely but pretending to be tired, dragging their feet on

the floor. Figure 3 shows pictures of all these evaluated exercises properly performed. The number of repetitions varies according to the age of volunteers, being the volunteers included in

A and B ranges who performed the highest amount of repetitions. Volunteers made four times each exercise series. Two of these series consisted in the corresponding exercises properly

performed and, in the other two series, the exercises were wrongly done. CALIBRATION OF LEGS In the human motion analysis field, it is common to use the information of the joints location or

orientation19,20,21,28. Thus, PHYTMO also includes three motions for the calibration of joints. The volunteers performed the following three motions in order to have information to

calibrate their lower-limbs: * Hip circles: standing, keeping the hip still and maintaining the legs completely straight, the volunteers performed circles with one leg, with the center of

rotation (COR) of hips stable (see Hip COR in Fig. 4). With this motion, the COR can be estimated using algorithms as different works propose29,30,31. * Hip frontal flex-extensions:

standing, keeping the hip still and maintaining the legs completely straight again, the volunteers moved this leg in a forward-backwards motion (see Hip axis ⊥ sagittal in Fig. 4). With this

motion, the axis perpendicular to the sagittal plane can be determined using different methods in the literature32,33. * Knee frontal flex-extensions: sat on a stable surface, the

volunteers moved the shin of one leg from its knee in a forward-backwards motion with a low range of motion, around 30°, keeping the knee still (see Knee axis in Fig. 4). With this motion,

the location of the axis can be determined using some proposed methods31,32,33. Similar motions were performed in order to calibrate the volunteers’ upper-limbs, so the aforementioned

methods, which are suggested for the inertial calibration of lower-limbs, can also be applied in this scenario. In this case, volunteers performed the following three motions: * Shoulder

circles:keeping the shoulder still and one arm completely straight, the volunteers performed circles with this arm (see Shoulder COR in Fig. 4). * Shoulder frontal flex-extensions: keeping

the shoulder still and maintaining one arm completely straight, the volunteers moved it in a forward-backwards motion (see Shoulder axis ⊥ sagittal in Fig. 4). * Elbow frontal

flex-extensions: keeping the elbow still and the hand in the supine position, the volunteers moved one forearm from its elbow in a forward-backwards motion with a low range of motion, around

60° (see Elbow axis in Fig. 4). DATA PROCESSING As previously mentioned, we synchronized the four IMUs through the identification of significant events in the recorded signals during

specific motions at the beginning of each recording. We used _MATLAB R2020b_34 to manually select the time instants of negligible turn rate recorded with each sensor in order to set the

initial time of each signal. We exported the data of each sensor separately in CSV format. More information about the name and organization of data is provided in the following section. With

respect to the optical system data, we used the _Motive 2.2.0_ software35 to fill gaps caused by occlusions during the data recording, so we provide the raw and interpolated optical data.

Firstly, we corrected the mislabeled markers in their current location. Secondly, we interpolated using the _Model based_ option, which uses the information of the visible markers of an

object to infer the trajectory of the others. Finally, we used the _Cubic_ interpolation in order to fill the information of markers in which the previous interpolation technique did not

work because less than three markers of an object were seen. This technique uses a cubic spline to fill the missing data. DATA RECORDS RAW DATA All raw data files exported from both inertial

and optical systems were stored as CSV files and have been uploaded to Zenodo36. A total of 7,076 files are available with https://doi.org/10.5281/zenodo.6319979. Files are called with the

nomenclature _GNNEEELP_S_, where _G_ refers to the letter of the range of age, so it is “A”, “B”, “C”, “D” or “E” (see Table 1); _NN_ is number of identification of the volunteer, which

ranges from “01” to “10”; _EEE_ indicates the type of exercise (KFE, HAA, SQT, EAH, EFE or SQZ) or gait variation (GAT, GIS or GHT); _L_ is the leg with the exercise is performed, so this

letter is only included in the KFE and HAA exercises and it can be “L” or “R”; _P_ is a label that indicates the evaluation of the exercise performance, which takes the “0” value when the

file contains the correctly performed exercise and “1” when exercises are wrongly performed; and finally, _S_ indicates the index of the series, being “1” for the first recorded series and

“2” for the second one. In this way, if the third (03) recorded volunteer aged between 41 and 50 years old (C) performs the knee flex-extension (KFE) of the right leg (R) following the

prescriptions of the exercise (0) in the first series (1), the corresponding file is called “C03KFER0_1.csv”. The main directory includes three folders called “inertial”, “optical raw” and

“optical interp”, which contain the data recorded with the IMUs and with the optical system, respectively. The “raw” and the “interp” folders contain the raw and interpolated data,

respectively. The data organization is different for the inertial and for the optical data, so they are separately explained bellow. On the one hand, the “inertial” folder is divided into

two directories which refer to the two possible group of limbs, that is “upper” or “lower”. The corresponding internal structure is schematized in Fig. 5 and detailed in the following. Each

limb directory contains five folders (“A”, “B”, “C”, “D” and “E”), corresponding to each age group of volunteers. The age group folders contain one directory for each limb segment, which

follows the names given to the IMUs. Thus, there are four directories for the lower-limbs (“Lshin”, “Lthig”, “Rshin” and “Rthigh”) and other four for the upper-limbs (“Lforearm”, “Larm”,

“Rforearm” and “Rarm”). On the other hand, the other two folders, “optical raw” and “optical interp”, contain two directories: “biomech_model” and “rigid_bodies”, whose internal structure is

schematized in Fig. 6. The data of the markers placed directly on the body of each subject are contained in the “biomech_model” folder and the data of the IMU mounting boards are located in

the “rigid_bodies” folder. Thus, “biomech_model” has only one folder, called “lower” to indicate that it corresponds to the lower-limbs, and this folder contains the five directories of

each age group. Conversely, the “rigid_bodies” folder is divided into the two directories “upper” and “lower”, with the corresponding age group folders (see Fig. 6). Since the optical system

includes the orientation and location of all the recorded objects in only one file, these data are not organized in segment folders but are included directly in the age group directories.

Inertial systems, whose final CSV files organization is indicated in Fig. 5, measure the turn rate, linear acceleration and magnetic field with the timestamp. This information is labeled in

the inertial files as “Time (s)”, “GyroscopeA (deg/s)”, “AccelerometerA (g)” and “MagnetometerA (uT)”, where “A” refers to the corresponding measurement axis (X, Y or Z). The information

given by each sensor is detailed in Table 3. Additionally, we provide the reference data of the optical system during the six repetitive exercises, i.e. the orientation and location in the

3D space of the IMU mounting boards. These files include three rows for the explanation of the recorded data. The first row indicates if the information corresponds to an IMU mounting board

(Rigid Body) or to a marker (Rigid Body Marker). The second row contains the name of the corresponding Rigid Body. We defined four rigid bodies, named “shin” and “thigh” for the structures

placed at the right side of the body and “shin2” and “thigh2” for the ones on the left side. The same mounting structures were used on legs and arms, placing always the “shin” named

structures on the shin or forearm, and the “thigh” on the thigh or arm, according to the recorded exercise. In the Rigid Body Marker case, the second row indicates the number of marker as

“Rigid Body: MarkerNum”, e.g. “shin:Marker1” corresponds to the marker labeled as “1” by the optical system that is part of the Rigid Body “shin”. Finally, the third row distinguishes

between the Rotation and Position information. The Rotation of the Rigid Bodies with respect to their initial position (see Fig. 1) is provided in quaternions (note that markers do not have

rotation information). The Position of the Rigid Bodies and the Rigid Body Markers, referred to the coordinates origin of the optical system, have magnitude of meters, as detailed in the

“OBJ/position” and “OBJ/orientation” rows in Table 3. PHYTMO also includes an extra file of the biomechanical model during the calibration of the lower-limbs of volunteers. In this way, we

provide relevant anthropometric information that can be needed or used in the development of IMU-based algorithms for motion monitoring, as previous works propose19,20,21,28. The data of the

lower-limb segments are indicated with the label “Bone” and when the data are referred to the position of markers, the corresponding label is “Bone Marker”. We use the Rizzoli Lower Body

Markerset37, so the markers are called according to its protocol. The position of the Bones and the Bone Markers is measured in meters, as detailed in the “SKT/position” and

“SKT/orientation” rows in Table 3. The names of skeletons are the group and identifier of the volunteer, as _GNN_, following the rules of the previously explained for the _GNNEEELP_S_

nomenclature. As an example, one representation of the inertial data during an exercise performed by one of the volunteers is depicted in Fig. 7. We show the signals obtained with the

tri-axial gyroscope, accelerometer and magnetometer of _Lshin_ during the KFEL exercise when it is correctly performed and when the volunteer performed it wrongly. Figure 7 shows that the

correctly and wrongly performed exercises are alike, following approximately similar patterns. However, the former is regular, the nine signals have similar amplitude in repetitions, whereas

the latter shows changes between repetitions. Also, differences in the repetition duration and the magnitude of the signals can be observed. The features of these signals are studied in the

following section in order to quantify these differences in performance. PROCESSED DATA The files from the inertial system are provided in raw format, synchronized as previously explained.

These signals contain relevant information for discerning the two kinds of performance of exercises: correct and wrong. One possible approach to extract information from the inertial system

signals is to split them into repetitions of the exercises and analyze different features as the average, standard deviation, maximum and minimum of these segments. One example of this

analysis is represented in Fig. 8, which shows some features of the three signals obtained with the accelerometer in _Lshin_ through _boxplots_. Boxplots are standardized for displaying

features using five numbers: minimum, maximum (both excluding the outliers), median, first quartile and third quartile. The outliers are also commonly depicted beyond the maximum and

minimum. Thus, the boxplots in Fig. 8 depict the quartiles and outliers of four features extracted from the acceleration signal of each repetition of the KFEL exercise correctly and wrongly

performed by A02. The features analyzed with these boxplots are the average, standard deviation, maximum and minimum. Figure 8 also shows the features of the correct exercises and the wrong

ones. In this way, we can do not only a comparison between the two kinds of performance, but also study the dynamics during both performances. The correct exercises show a low dispersion of

data, with smaller boxes than those which correspond to the wrong performance. That is consistent with the signals depicted in Fig. 7, which have similar amplitudes in the correct

performance and high differences in its wrong performance. Furthermore, the median commonly differs between the two kinds of performance. Thus, the differences among these data make possible

the classification between a correctly and wrongly performed exercise. TECHNICAL VALIDATION SENSOR PLACEMENT Volunteers wore tight and sporty clothing for the experiments in order to

prevent sensor movement and limitations in the exercises performance. As described in the Methods (see Methods/Participants and Fig. 3), the wearable sensors were placed on the legs or arms,

according to the recording of an upper-or lower-limb exercise, and always with the X_I_-axis pointed to the ceiling when volunteers were standing up. Sensors and optical markers were placed

by the same two researchers to ensure consistency. It is also remarkable that previous to each recording, the optical system was calibrated, obtaining an error lower than 1 mm in all cases.

MISSING DATA Optical systems commonly loose data because of occlusions. We provide the raw and the filled data, in which we interpolate the missing data, in order to ease its use. The raw

data include a 6.9% of occlusions and we reduce until a 1.3% of them when the data were interpolated. The interpolated files still have some missing data due to occlusions larger than 2

seconds in the raw data that were not accurately interpolated, as happened during the performance of the SQT exercise by some subjects who accidentally covered one or two IMUs during the

exercise performance. We provide those files in which the data of some mounting boards are recorded as Rigid Bodies and other are missed. The recorded boards are given raw and interpolated

along the complete recording. When there were recording problems with the inertial system, we removed the erroneous data in PHYTMO (5%). As a consequence, some volunteers have less recorded

exercises. COMPARISON WITH PUBLISHED DATA SETS This database focuses on the study of the motion monitoring during the performance of physical therapies, which is a relevant topic of growing

interest. This is a remarkable distinction, since the previous databases related with the human motion monitoring commonly study walk patterns and variations16,38,39, activities of daily

living40,41,42,43 or are specific for different sports, such as football44 or karate45, but few data of prescribed exercises are publicly available. Besides this lack of data, the existing

physical exercises databases aimed for physical therapies are recorded with optical systems, such as three-dimensional systems and RGB together with depth sensors46, or only with the

RGB-depth systems47. This implies a limitation for the physical therapies that can only be performed in controlled environments where no occlusions occur. For the best of our knowledge, no

databases of alternatives based on wearables are available. Regarding the number of participants, this database includes a total of 30 subjects whereas in previous studies of specific

exercises less than 10 subjects were recorded. Thus, PHYTMO contains data for the analysis of the inertial-based monitoring of physical therapies with a high variability of participants.

Related to this topic, data can be used in ML-based studies for classification purposes, such as the identification of a set of exercises48. As a matter of fact, this database has been used

for the simultaneous recognition and evaluation of exercises in physical routines18. Another possible use is the study of motion kinematics, focused on the raw inertial data or aiming to

evaluate joint angles, an important parameter during these prescribed exercises46. Since physical therapies also include different variations of gait, PHYTMO contributes to the study of

walking patterns with inertial sensors. Natural gait and gait variations study is of great interest16,38 and, as previously mentioned, databases focused on gait already exist, but they are

commonly obtained only with optical sensors38,39. Conversely, we provide data obtained with magneto-inertial sensors, including 3 different variations of gait not available yet in previous

datasets. USAGE NOTES Different studies in the literature show that IMUs are valid for human motion monitoring, for the description of movements and for their evaluation. To ensure a huge

usability of this database, the provided files are in CSV format, so they can be easily imported into Python or MATLAB. This work contributes to the human motion analysis field by overcoming

the lack of available data to compare algorithms. The validation of the proposals is so far performed on different data obtained for each of the studies. This difference makes it impossible

to draw a fair comparison between them and, sometimes, not even the same metrics are available to compare them. This database helps to the development of new alternatives for motion

analysis, by using the inertial data provided, and to their validation by using the reference data from the optical system. In this way, these data contribute to assess the strengths and

limitations of various proposals. In addition, data are provided so that the research process does not require a previous step of collecting volunteers and recording movements in motion

capture laboratories, and the data can be used directly. Python tools are available, specifically for the analysis of the gait variations, which can be processed with GaityPy, which are

Python functions to read accelerometry data and estimate gait features (https://pypi.org/project/gaitpy/). MATLAB also have tools for human motion analysis: the Kinematic and Inverse

Dynamics toolbox (https://www.mathworks.com/matlabcentral/fileexchange/72863-forward-and-inverse-kinematics) can be used to study human kinematics and dynamics, and the Inertial Measurement

Unit position calculator (https://www.mathworks.com/matlabcentral/fileexchange/25730-inertial-measurement-unit-position-calculator) can be applied for calculating the body’s trajectory,

velocity and attitude using data from an IMU as input. Additionally, the presented IMU data can be used in OpenSim49, a freely available tool for musculoskeletal modeling and dynamic

simulation of motions. Specifically, OpenSim has a workflow called OpenSense (https://simtk-confluence.stanford.edu/display/OpenSim/OpenSense+-+Kinematics+with+IMU+Data) that details the

corresponding data processing to simulate the kinematics of the body using the measurements of IMUs. This database contributes in other human monitoring fields different from the motion

analysis. The data include different exercises and subject variability, which are also labeled for different classification applications. Some of these classifications can be human activity

recognition, motion evaluation, or optimization of localization and number of sensors. It should be noted that the sensors have always been placed in the same body area, but not in a

specific anatomical landmark. This is important to mention, since the data included cannot be used in studies of the kinematics of different populations, although they are useful for the

development of movement analysis and monitoring algorithms, contributing in these motion monitoring areas. Finally, we help to its usability in this classification problems with the

publication of the _features_extraction_ function, developed for MATLAB in any of its version. This function splits signals using a sliding window, returning its segments, and extract signal

features, in the time and frequency domain, based on prior studies of the literature6,10,11,48,50. This function needs at least three signals from one triaxial sensor to extract their

features, a window size in number of samples and a window shift that defines the distance between consecutive windows. The returning features are divided into the time and frequency domains.

The time domain features are: mean of each signal, maximum of each signal, minimum of each signal, mean of the absolute value of each signal, standard deviation of each signal, variance of

each signal, mean absolute deviation of each signal, root mean square of each signal, mean over the three axes of each sensor, average standard deviation over each three axes, skewness of

each signal, average skewness over each three axes, kurtosis of each signal, average kurtosis over each three axes, 25, 50 and 75 quartiles of each signal, power of each signal, correlation

between the three axes, correlation of each axis and the vector norm and entropy of each signal. The frequency domain features are always for each signal and they are the following: energy,

absolute value of the maximum FFT coefficient, absolute value of the minimum FFT coefficient, maximum FFT coefficient, mean FFT coefficient, median FFT coefficient, discrete cosine transform

and spectral entropy. Finally, a box plot of the selected features and sensor is depicted to ease the analysis of data in PHYTMO. CODE AVAILABILITY No custom code was used to generate or

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Vecteezy. Free Vector Art. https://www.vecteezy.com/ (2022). Download references ACKNOWLEDGEMENTS The authors would like to thank Andrea MartÃnez-Parra and F. Javier Redondo-GarcÃa for their

collaboration in the measurement campaign and data processing, and the volunteers that performed the exercises to create this database. This work was supported by Junta de Comunidades de

Castilla La Mancha (FrailCheck SBPLY/17/180501/000392), the Spanish Ministry of Science, Innovation and Universities (MICROCEBUS RTI2018-095168-B-C51) and Comunidad de Madrid (RACC

CM/JIN/2021-016). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * University of Alcala, Department of Electronics, Alcalá de Henares, 28801, Spain Sara García-de-Villa, Ana Jiménez-Martín 

& Juan Jesús García-Domínguez Authors * Sara García-de-Villa View author publications You can also search for this author inPubMed Google Scholar * Ana Jiménez-Martín View author

publications You can also search for this author inPubMed Google Scholar * Juan Jesús García-Domínguez View author publications You can also search for this author inPubMed Google Scholar

CONTRIBUTIONS S.G.V., A.J.M. and J.J.G.D. conceived the experiments, S.G.V. conducted the experiments and processed the data, and together with A.J.M. and J.J.G.D. analyzed the results. All

authors reviewed the manuscript. CORRESPONDING AUTHOR Correspondence to Sara García-de-Villa. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL

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To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE García-de-Villa, S., Jiménez-Martín, A. &

García-Domínguez, J.J. A database of physical therapy exercises with variability of execution collected by wearable sensors. _Sci Data_ 9, 266 (2022).

https://doi.org/10.1038/s41597-022-01387-2 Download citation * Received: 16 September 2021 * Accepted: 12 May 2022 * Published: 03 June 2022 * DOI: https://doi.org/10.1038/s41597-022-01387-2

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