To enable the evaluation of HPE models and the development of exercise feedback systems, we produced a new rehabilitation dataset (REHAB24-6). The main focus is on a diverse range of exercises, views, body heights, lighting conditions, and exercise mistakes. With the publicly available RGB videos, skeleton sequences, repetition segmentation, and exercise correctness labels, this dataset offers the most comprehensive testbed for exercise-correctness-related tasks.

Contents

• 65 recordings (184,825 frames, 30 FPS):
  • RGB videos from two cameras (videos.zip, horizontal = Camera17, vertical = Camera18);
  • 3D and 2D projected positions of 41 motion capture marker (<2/3>d_markers.zip, marker labels in marker_names.txt);
  • 3D and 2D projected positions of 26 skeleton joints (<2/3>d_joints.zip, joint labels in joint_names.txt);
• Annotation of 1,072 exercise repetitions (Segmentation.csv, indexed based only on 30 FPS data, described in Segmentation.txt):
  • Temporal segmentation (start/end frame, most between 2–5 seconds);
  • Binary correctness label (around 90 from each category in each exercise, except Ex3 with around 50);
  • Exercise direction (around 90 from each direction in each exercise);
  • Lighting conditions label.

Recording Conditions

Our laboratory setup included 18 synchronized sensors (2 RGB video cameras, 16 ultra-wide motion capture cameras) spread around an 8.2 × 7 m room. The RGB cameras were located in the corners of the room, one in a horizontal position (hor.), providing a larger field of view (FoV), and one in a vertical (ver.), resulting in a narrower FoV. Both types of cameras were synchronized with a sampling frequency of 30 frames per second (FPS).

The subjects wore motion capture body suits with 41 markers attached to them, which were detected by optical cameras. The OptiTrack Motive 2.3.0 software inferred the 3D positions of the markers in virtual centimeters and converted them into a skeleton with 26 joints, forming our human pose 3D ground truth (GT).

To acquire a 2D version of the ground truth in pixel coordinates, we applied a projection of the virtual coordinates into the camera using the simplified pinhole model. We estimated the parameters for this projection as follows. First, the virtual position of the cameras was estimated using measuring tape and knowledge of the virtual origin. Then, the orientation of the cameras was optimized by matching the virtual marker positions with their position in the videos.

We also simulated changes in lighting conditions: a few videos were shot in the natural evening light, which resulted in worse visibility, while the rest were under artificial lighting.

Exercises

10 subjects participated in our recording and consented to release the data publicly: 6 males and 4 females of different ages (from 25 to 50) and fitness levels. A physiotherapist instructed the subjects on how to perform the exercises so that at least five repetitions were done in what he deemed the correct way and five more incorrectly. The participants had a certain degree of freedom, e.g., in which leg they used in Ex4 and Ex5. Similarly, the physiotherapist suggested different exercise mistakes for each subject.

• Ex1 = Arm abduction: sideway raising of the straightened right arm;
• Ex2 = Arm VW: fluent transition of arms between V (arms straight up) and W (elbows down, hands up) shape;
• Ex3 = Push-ups: push-ups with hands on a table;
• Ex4 = Leg abduction: sideway raising of the straightened leg;
• Ex5 = Leg lunge: pushing a knee of the back leg down while keeping a right angle on the front knee;
• Ex6 = Squats.

Every exercise was also executed in two directions, resulting in different views of the subject depending on the camera. Facing the horizontal camera resulted in a front view for that camera and a profile from the other. Facing the wall between the cameras shows the subject from half-profile in both cameras. A rare direction, only used for push-ups due to the use of the table, was facing the vertical camera, with the views being reversed compared to the first orientation.

Citation

Cite the related conference paper:

Černek, A., Sedmidubsky, J., Budikova P.: REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods. 17th International Conference on Similarity Search and Applications (SISAP). Springer, 14 pages, 2024.

License

This dataset is for academic or non-profit organization noncomercial research use only. By using you agree to appropriately reference the paper above in any publication making of its use. For comercial purposes contact us at info@visioncraft.ai

Note:

Because this dataset has been used in a competition, we had to hide some of the data to prepare the test dataset for the competition. Thus, in the previous version of the dataset, only train.csv file is existed.

Content

This dataset represents 10 different physical poses that can be used to distinguish 5 exercises. The exercises are Push-up, Pull-up, Sit-up, Jumping Jack and Squat. For every exercise, 2 different classes have been used to represent the terminal positions of that exercise (e.g., “up” and “down” positions for push-ups).

Collection Process

About 500 videos of people doing the exercises have been used in order to collect this data. The videos are from Countix Dataset that contain the YouTube links of several human activity videos. Using a simple Python script, the videos of 5 different physical exercises are downloaded. From every video, at least 2 frames are manually extracted. The extracted frames represent the terminal positions of the exercise.

Processing Data

For every frame, MediaPipe framework is used for applying pose estimation, which detects the human skeleton of the person in the frame. The landmark model in MediaPipe Pose predicts the location of 33 pose landmarks (see figure below). Visit Mediapipe Pose Classification page for more details.

https://mediapipe.dev/images/mobile/pose_tracking_full_body_landmarks.png" alt="33 pose landmarks">

Background: The benefits to be obtained from home-based physical therapy programmes are dependant on the proper execution of physiotherapy exercises during unsupervised treatment. Different types of instructions, such as videotaped demonstrations and brochures, may influence patient performance at home. Methods: A total of thirty healthy volunteers (mean age of 31 years) had their movements captured using wearable inertial sensors, after video recordings of five different exercises with varying levels of complexity were demonstrated to them. Participants were then randomised to receive videotaped, written or illustrated instructions, along with wearable sensors to enable a second unsupervised data capture at home. Movement consistency between the participants’ recordings was assessed with metrics of movement smoothness, intensity, consistency and control. Results: Irrespective of group allocation, subjects executed all the exercises consistently when recording at home and as compared with their performance in the lab. However, all the considered modes of instruction were ineffective in setting the pace of movement, as participants executed all movements faster compared to the demonstrated video footages. Conclusion: Since patient performance is unaffected by the type of the prescribed instructions, healthcare professionals may recommend instructive material based on patient preference in order to improve satisfaction levels. A wearable system with real-time performance metrics and feedback would better aid in therapeutic exercises that ought to be performed with appropriate speed, intensity, smoothness and range of motion.

An observational, descriptive and cross-sectional study was carried out among Physiotherapists collegiates in Spain, who have worked for at least 3 months in direct care with patients. The aim of this study is to know the percentage of physiotherapists suffering from type two violence (sexual, physical or psychological/verbal violence in their clinical role) in Spain, as well as professional, clinical or personal variables that might be related to violence prevalence against physiotherapists by patients or their relatives/companions. In addition, the responses offered by the physical therapists and their perception of the results obtained have been consulted, as well as any personal consequences at health and work.

The study was emailed though Physiotherapists´ Colleges and/or disseminated through their webs from January to March 2022. Data were collected through an online form. After being informed of the objectives of the study, they voluntarily completed the data though an anonymous questionnaire. Data confidentiality was ensured through the use of Microsoft Forms software (Microsoft Office, Microsoft Corporation, USA) pursuant to an agreement with the University of A Coruña. The whole description of the methodology used has been published in: “Elaboración de un cuestionario sobre violencia(s) sufrida(s) por profesionales del ámbito de la Fisioterapia” [Developing a questionnaire about violence(s) suffered by professionals in the field of Physiotherapy] in Revista espanola de salud publica 97: e202306048 (2023-06-09). PMID: 37293946. ISSN (electronic): 2173-9110.

Additional related data collected that was not included in the current data package:2.942 respondents who agreed to participate and who had treated patients were obtained, but 9 answers were eliminated because of inconsistence in answers or because few cases of sex other were present, resulting 2.933 cases. In addition, some changes in presentation of data has been made: information on the autonomous community of origin and description of violent episodes by physiotherapists in their own words were eliminated. Age and clinical experience were collected in years but have been regrouped due to ethical restrictions; also, practice settings with low number of responses were included in the category “others” in order to protect anonymity.

Responses with inconsistencies (for example, between age and clinical experience) were excluded. Due to ethical restrictions, some questions have been eliminated or regrouped to guarantee anonymity.

Keywords: Workplace Violence, Prevalence, Physical Therapy, Risk factors (associated factors); Sexual harassment, physical abuse, job satisfaction

Information about funding sources or sponsorship that supported the collection of the data: This work has been sponsored by the General Council of Colleges of Physiotherapists in Spain.[Consejo General de Colegios de Fisioterapeutas de España]. Resources from Universidade da Coruña (University of A Coruna) have been employed. It has been also supported by MICINN grant PID2020-113578RB-I00, the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14). We wish to acknowledge the support received from the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union.

Citation for and links to publications that cite or use the data: Boo-Mallo, T., Pérez-Caramés, A., Domínguez-Rodríguez, A., Oviedo-de-la-Fuente, M., Martínez-Rodríguez, A. Violence towards physical therapists in Spain, database from a national survey. Zenodo. https://doi.org/10.5281/zenodo.10599701

Telehealth has helped to increase access to rehabilitative services such as occupational and physical therapy. The early COVID-19 pandemic amplified the need for remote access to care, and the rapid implementation of telehealth systems provided a unique opportunity to learn from clinicians’ experiences adopting telehealth for telerehabilitation applications. To understand these experiences, a self-administered online survey was conducted to capture perspectives on ease of telerehabilitation use and adoption from occupational and physical therapists. The survey captured retrospective views on telerehabilitation use pre-pandemic as well as real-time perspectives on telerehabilitation during the early stages of the pandemic (July to August 2020). The survey gathered information on clinician demographics (N = 109), clinicians’ experiences with adopting or utilizing telerehabilitation systems, and their perceptions on remotely performing cognitive, emotional, and physical assessments via video-conferencing (a common mode of telehealth). Responses demonstrated a modest increase in telerehabilitation as a care setting (rate increase from 3.4% to 19.3%), and telerehabilitation was more generally tried during the early stages of the pandemic (41 clinicians explicitly reported telerehabilitation use). However, technology access and acceptance remained low, with 38 clinicians (35%) expressing concerns that technology was ineffective or impractical, unavailable, not covered by insurance, or not desired by their patients. Video-conferencing technology was perceived as generally ill-equipped to support clinicians in performing remote assessment tasks. Physical assessment tasks were considered particularly difficult, with 55% of clinicians rating their ability to perform these tasks in the range of moderately difficult to unable to perform. To address these difficulties and better augment clinical care, clinicians require more robust assessment methods that may combine video, mobile, and wearable technologies that would be accessible to a patient at home. When designing future telerehabilitation tools, information captured through these modes must be task-relevant, standardized, and understandable to a remote clinician.

A Gold Standard Corpus for Activity Information

Dataset Title: A Gold Standard Corpus for Activity Information (GoSCAI)

Dataset Curators: The Epidemiology & Biostatistics Section of the NIH Clinical Center Rehabilitation Medicine Department

Dataset Version: 1.0 (May 16, 2025)

Dataset Citation and DOI: NIH CC RMD Epidemiology & Biostatistics Section. (2025). A Gold Standard Corpus for Activity Information (GoSCAI) [Data set]. Zenodo. doi: 10.5281/zenodo.15528545

EXECUTIVE SUMMARY

This data statement is for a gold standard corpus of de-identified clinical notes that have been annotated for human functioning information based on the framework of the WHO's International Classification of Functioning, Disability and Health (ICF). The corpus includes 484 notes from a single institution within the United States written in English in a clinical setting. This dataset was curated for the purpose of training natural language processing models to automatically identify, extract, and classify information on human functioning at the whole-person, or activity, level.

CURATION RATIONALE

This dataset is curated to be a publicly available resource for the development and evaluation of methods for the automatic extraction and classification of activity-level functioning information as defined in the ICF. The goals of data curation are to 1) create a corpus of a size that can be manually deidentified and annotated, 2) maximize the density and diversity of functioning information of interest, and 3) allow public dissemination of the data.

LANGUAGE VARIETIES

Language Region: en-US

Prose Description: English as written by native and bilingual English speakers in a clinical setting

LANGUAGE USER DEMOGRAPHIC

The language users represented in this dataset are medical and clinical professionals who work in a research hospital setting. These individuals hold professional degrees corresponding to their respective specialties. Specific demographic characteristics of the language users such as age, gender, or race/ethnicity were not collected.

ANNOTATOR DEMOGRAPHIC

The annotator group consisted of five people, 33 to 76 years old, including four females and one male. Socioeconomically, they came from the middle and upper-middle income classes. Regarding first language, three annotators had English as their first language, one had Chinese, and one had Spanish. Proficiency in English, the language of the data being annotated, was native for three of the annotators and bilingual for the other two. The annotation team included clinical rehabilitation domain experts with backgrounds in occupational therapy, physical therapy, and individuals with public health and data science expertise. Prior to annotation, all annotators were trained on the specific annotation process using established guidelines for the given domain, and annotators were required to achieve a specified proficiency level prior to annotating notes in this corpus.

LINGUISTIC SITUATION AND TEXT CHARACTERISTICS

The notes in the dataset were written as part of clinical care within a U.S. research hospital between May 2008 and November 2019. These notes were written by health professionals asynchronously following the patient encounter to document the interaction and support continuity of care. The intended audience of these notes were clinicians involved in the patients' care. The included notes come from nine disciplines - neuropsychology, occupational therapy, physical medicine (physiatry), physical therapy, psychiatry, recreational therapy, social work, speech language pathology, and vocational rehabilitation. The notes were curated to support research on natural language processing for functioning information between 2018 and 2024.

PREPROCESSING AND DATA FORMATTING

The final corpus was derived from a set of clinical notes extracted from the hospital electronic medical record (EMR) for the purpose of clinical research. The original data include character-based digital content originally. We work in ASCII 8 or UNICODE encoding, and therefore part of our pre-processing includes running encoding detection and transformation from encodings such as Windows-1252 or ISO-8859 format to our preferred format.

On the larger corpus, we applied sampling to match our curation rationale. Given the resource constraints of manual annotation, we set out to create a dataset of 500 clinical notes, which would exclude notes over 10,000 characters in length.

To promote density and diversity, we used five note characteristics as sampling criteria. We used the text length as expressed in number of characters. Next, we considered the discipline group as derived from note type metadata and describes which discipline a note originated from: occupational and vocational therapy (OT/VOC), physical therapy (PT), recreation therapy (RT), speech and language pathology (SLP), social work (SW), or miscellaneous (MISC, including psychiatry, neurology and physiatry). These disciplines were selected for collecting the larger corpus because their notes are likely to include functioning information. Existing information extraction tools were used to obtain annotation counts in four areas of functioning and provided a note’s annotation count, annotation density (annotation count divided by text length), and domain count (number of domains with at least 1 annotation).

We used stratified sampling across the 6 discipline groups to ensure discipline diversity in the corpus. Because of low availability, 50 notes were sampled from SLP with relaxed criteria, and 90 notes each from the 5 other discipline groups with stricter criteria. Sampled SLP notes were those with the highest annotation density that had an annotation count of at least 5 and a domain count of at least 2. Other notes were sampled by highest annotation count and lowest text length, with a minimum annotation count of 15 and minimum domain count of 3.

The notes in the resulting sample included certain types of PHI and PII. To prepare for public dissemination, all sensitive or potentially identifying information was manually annotated in the notes and replaced with substituted content to ensure readability and enough context needed for machine learning without exposing any sensitive information. This de-identification effort was manually reviewed to ensure no PII or PHI exposure and correct any resulting readability issues. Notes about pediatric patients were excluded. No intent was made to sample multiple notes from the same patient. No metadata is provided to group notes other than by note type, discipline, or discipline group. The dataset is not organized beyond the provided metadata, but publications about models trained on this dataset should include information on the train/test splits used.

All notes were sentence-segmented and tokenized using the spaCy en_core_web_lg model with additional rules for sentence segmentation customized to the dataset. Notes are stored in an XML format readable by the GATE annotation software (https://gate.ac.uk/family/developer.html), which stores annotations separately in annotation sets.

CAPTURE QUALITY

As the clinical notes were extracted directly from the EMR in text format, the capture quality was determined to be high. The clinical notes did not have to be converted from other data formats, which means this dataset is free from noise introduced by conversion processes such as optical character recognition.

LIMITATIONS

Because of the effort required to manually deidentify and annotate notes, this corpus is limited in terms of size and representation. The curation decisions skewed note selection towards specific disciplines and note types to increase the likelihood of encountering information on functioning. Some subtypes of functioning occur infrequently in the data, or not at all. The deidentification of notes was done in a manner to preserve natural language as it would occur in the notes, but some information is lost, e.g. on rare diseases.

METADATA

Information on the manual annotation process is provided in the annotation guidelines for each of the four domains:

- Communication & Cognition (https://zenodo.org/records/13910167)

- Mobility (https://zenodo.org/records/11074838)

- Self-Care & Domestic Life (SCDL) (https://zenodo.org/records/11210183)

- Interpersonal Interactions & Relationships (IPIR) (https://zenodo.org/records/13774684)

Inter-annotator agreement was established on development datasets described in the annotation guidelines prior to the annotation of this gold standard corpus.

The gold standard corpus consists of 484 documents, which include 35,147 sentences in total. The distribution of annotated information is provided in the table below.

The datasets are linked to the manuscript titled; ‘The effectiveness of psychologically-informed physiotherapy for people with neck pain and the mediating role of illness perceptions: A replicated single-case experimental design study'. This replicated single-case study was conducted to explore the effectiveness of a personalised intervention addressing unhelpful illness perceptions and dysfunctional movement behaviour in people with non-specific neck pain, and to explore the mediating role of changes in illness perceptions. More specifically, this study aimed to explore: (1) whether global perceived effect, function, pain and self-efficacy improved during and after psychologically-informed physiotherapy, (2) whether changes in illness perceptions mediate the effect of the intervention and (3) whether specific personal or neck pain related factors moderate the effect of the intervention. The first dataset contains clinical data of repeated measurements of several quantitative outcome variables in a sample of 14 patients with non-specific neck pain and a risk profile for chronicity (Start Neck Tool score≥4). Measurements included: (a) Demographic and neck pain related data (i.e., gender, age, height, bodyweight, education level, work status, duration and onset of their neck pain (gradual or sudden, and if sudden, history of trauma), disability (using the Neck Disability Index) and risk profile (using the StartNeckTool). These data were used to select and describe the study sample. (b) Primary and secondary outcome measurements (i.e., respectively Global Perceived Effect (using the Global Perceived Effect Scale), Function (using the Patient Specific Functioning Scale for two self-selected activities; PSFS-1 and PSFS-2), Pain Intensity (using the Numeric Pain Rating Scale (NPRS) and Self-efficacy (using the Pain Self Efficacy Questionnaire-4 item version (PSEQ-4)) and (c) Data regarding the proposed mediating factors (i.e., Illness Perceptions, evaluated with the Brief Illness Perception Questionnaire (B-IPQ) and the Fear Avoidance Beliefs Questionnaire-physical activity subscale). These measurements were conducted during (A) a pre-intervention baseline phase of two weeks, (B) an intervention-phase of 4-7 physiotherapy sessions offered once a week and (A’) an intervention-withdrawal phase. Outcomes were self-reported and measured twice a week from enrolment till the end of the intervention, with two follow-up measurements, at one week and at three months after the last treatment session. Thirteen physiotherapists from 12 primary care physiotherapy clinics in the Netherlands participated in the study. The 14 participants who were included in the study were treated by seven therapists. The characteristics of the participating therapists are specified in a second dataset. This dataset contains information regarding gender, age, work experience, professional master degrees and/or relevant courses of the participating therapists.

The datasets are linked to the manuscript titled; ‘The effectiveness of psychologically-informed physiotherapy for people with neck pain and the mediating role of illness perceptions: A replicated single-case experimental design study'. This replicated single-case study was conducted to explore the effectiveness of a personalised intervention addressing unhelpful illness perceptions and dysfunctional movement behaviour in people with non-specific neck pain, and to explore the mediating role of changes in illness perceptions. More specifically, this study aimed to explore: (1) whether global perceived effect, function, pain and self-efficacy improved during and after psychologically-informed physiotherapy, (2) whether changes in illness perceptions mediate the effect of the intervention and (3) whether specific personal or neck pain related factors moderate the effect of the intervention. The first dataset contains clinical data of repeated measurements of several quantitative outcome variables in a sample of 14 patients with non-specific neck pain and a risk profile for chronicity (Start Neck Tool score≥4). Measurements included: (a) Demographic and neck pain related data (i.e., gender, age, height, bodyweight, education level, work status, duration and onset of their neck pain (gradual or sudden, and if sudden, history of trauma), disability (using the Neck Disability Index) and risk profile (using the StartNeckTool). These data were used to select and describe the study sample. (b) Primary and secondary outcome measurements (i.e., respectively Global Perceived Effect (using the Global Perceived Effect Scale), Function (using the Patient Specific Functioning Scale for two self-selected activities; PSFS-1 and PSFS-2), Pain Intensity (using the Numeric Pain Rating Scale (NPRS) and Self-efficacy (using the Pain Self Efficacy Questionnaire-4 item version (PSEQ-4)) and (c) Data regarding the proposed mediating factors (i.e., Illness Perceptions, evaluated with the Brief Illness Perception Questionnaire (B-IPQ) and the Fear Avoidance Beliefs Questionnaire-physical activity subscale). These measurements were conducted during (A) a pre-intervention baseline phase of two weeks, (B) an intervention-phase of 4-7 physiotherapy sessions offered once a week and (A’) an intervention-withdrawal phase. Outcomes were self-reported and measured twice a week from enrolment till the end of the intervention, with two follow-up measurements, at one week and at three months after the last treatment session. Thirteen physiotherapists from 12 primary care physiotherapy clinics in the Netherlands participated in the study. The 14 participants who were included in the study were treated by seven therapists. The characteristics of the participating therapists are specified in a second dataset. This dataset contains information regarding gender, age, work experience, professional master degrees and/or relevant courses of the participating therapists.

Home Health Care Agency Ratings

Quality Measurements, Types of Services and More

By US Open Data Portal, data.gov [source]

About this dataset

This dataset provides a list of all Home Health Agencies registered with Medicare. Contained within this dataset is information on each agency's address, phone number, type of ownership, quality measure ratings and other associated data points. With this valuable insight into the operations of each Home Health Care Agency, you can make informed decisions about your care needs. Learn more about the services offered at each agency and how they are rated according to their quality measure ratings. From dedicated nursing care services to speech pathology to medical social services - get all the information you need with this comprehensive look at U.S.-based Home Health Care Agencies!

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How to use the dataset

Are you looking to learn more about Home Health Care Agencies registered with Medicare? This dataset can provide quality measure ratings, addresses, phone numbers, types of services offered and other information that may be helpful when researching Home Health Care Agencies.

This guide will explain how to use the data in this dataset to gain a better understanding of Home Health Care Agencies registered with Medicare.

First, you will need to become familiar with the columns in the dataset. A list of all columns and their associated descriptions is provided above for your reference. Once you understand each column’s purpose, it will be easier for you to decide what metrics or variables are most important for your own research.

Next, use this data to compare various facets between different Home Health Care Agencies such as type of ownership, services offered and quality measure ratings like star rating or CMS certification number (from 0-5 stars). Collecting information from multiple sources such as public reviews or customer feedback can help supplement these numerical metrics in order to paint a more accurate picture about each agency's performance and customer satisfaction level.

Finally once you have collected enough data points on one particular agency or a comparison between multiple agencies then conduct more analysis using statistical methods like correlation matrices in order to determine any patterns that exist within the data set which may reveal valuable insights into topic of research at hand

Research Ideas

• Using the data to compare quality of care ratings between agencies, so people can make better informed decisions about which agency to hire for home health services.
• Analyzing the costs associated with different types of home health care services, such as nursing care and physical therapy, in order to determine where money could be saved in health care budgets.
• Evaluating the performance of certain agencies by analyzing the number of episodes billed to Medicare compared to their national averages, allowing agencies with lower numbers of billing episodes to be identified and monitored more closely if necessary

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: csv-1.csv | Column name | Description | |:----------------------------------------...

Here are a few use cases for this project:

1. Ergonomics Evaluation: This model can be used by OSHA/Ergonomics companies or consultants for evaluating employees' postures, particularly those in sedentary roles, to recommend improvements and reduce the risk of musculoskeletal disorders.

2. Health & Wellness Applications: The model can be used in apps that promote good posture, which may help individuals combat issues like back pain. These apps can provide automatic feedback on user's posture in real-time.

3. Virtual Fitness Training: In the context of fitness or yoga classes, the model can evaluate whether the participants are performing the postures correctly or not.

4. Physical Therapy: Physical therapists can use this model to create and monitor rehabilitation programs. They can give patients personalized, remote feedback during their recovery.

5. Gaming Industry: For interactive video games, the model can be used to observe and interpret players' postures to control in-game characters, adding another level of challenge and immersion.

The relationship between physical activity and mental health, especially depression, is one of the most studied topics in the field of exercise science and kinesiology. Although there is strong consensus that regular physical activity improves mental health and reduces depressive symptoms, some debate the mechanisms involved in this relationship as well as the limitations and definitions used in such studies. Meta-analyses and systematic reviews continue to examine the strength of the association between physical activity and depressive symptoms for the purpose of improving exercise prescription as treatment or combined treatment for depression. This dataset covers 27 review articles (either systematic review, meta-analysis, or both) and 365 primary study articles addressing the relationship between physical activity and depressive symptoms. Primary study articles are manually extracted from the review articles. We used a custom-made workflow (Fu, Yuanxi. (2022). Scopus author info tool (1.0.1) [Python]. https://github.com/infoqualitylab/Scopus_author_info_collection that uses the Scopus API and manual work to extract and disambiguate authorship information for the 392 reports. The author information file (author_list.csv) is the product of this workflow and can be used to compute the co-author network of the 392 articles. This dataset can be used to construct the inclusion network and the co-author network of the 27 review articles and 365 primary study articles. A primary study article is "included" in a review article if it is considered in the review article's evidence synthesis. Each included primary study article is cited in the review article, but not all references cited in a review article are included in the evidence synthesis or primary study articles. The inclusion network is a bipartite network with two types of nodes: one type represents review articles, and the other represents primary study articles. In an inclusion network, if a review article includes a primary study article, there is a directed edge from the review article node to the primary study article node. The attribute file (article_list.csv) includes attributes of the 392 articles, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network. Collectively, this dataset reflects the evidence production and use patterns within the exercise science and kinesiology scientific community, investigating the relationship between physical activity and depressive symptoms. FILE FORMATS 1. article_list.csv - Unicode CSV 2. author_list.csv - Unicode CSV 3. Chinese_author_name_reference.csv - Unicode CSV 4. inclusion_net_edges.csv - Unicode CSV 5. review_article_details.csv - Unicode CSV 6. supplementary_reference_list.pdf - PDF 7. README.txt - text file 8. systematic_review_inclusion_criteria.csv - Unicode CSV UPDATES IN THIS VERSION COMPARED TO V3 (Clarke, Caitlin; Lischwe Mueller, Natalie; Joshi, Manasi Ballal; Fu, Yuanxi; Schneider, Jodi (2023): The Inclusion Network of 27 Review Articles Published between 2013-2018 Investigating the Relationship Between Physical Activity and Depressive Symptoms. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4614455_V3) - We added a new file systematic_review_inclusion_criteria.csv.

Here are a few use cases for this project:

1. Fitness and Physical Therapy: The Human_Pose_Detection model can be used to track and analyze body postures during various exercises and physical therapy sessions, allowing trainers and therapists to monitor progress, provide feedback, and ensure that proper techniques are being utilized to prevent injuries.

2. Smart Home Automation: Integrating the model into smart home systems can improve user experience by adapting the environment according to the detected poses, such as adjusting lighting based on whether a person is sitting, standing or sleeping, or automating appliances based on detected activities like moving or eating.

3. Security and Surveillance: The model can be used to monitor public spaces or private properties for unusual activities or trespassers by analyzing human poses and detecting any suspicious behavior, such as prolonged sleeping in public areas or people running in restricted zones.

4. Elderly and Disabled Care: Utilizing the Human_Pose_Detection model within care facilities or home monitoring systems can assist caregivers in tracking the activities and movements of elderly or disabled individuals, enabling early detection of falls, unusual sleeping patterns or potential health issues.

5. Workplace Ergonomics: By analyzing employee postures in office or industrial settings, the model can help identify potential ergonomic issues or assess the effectiveness of current workplace layouts, enabling companies to improve safety, comfort and productivity for their employees.

Here are a few use cases for this project:

1. Personal Fitness Apps: The "Bench Press Detection" model can be integrated into fitness apps. It can provide real-time feedback on the user's form during the exercise, ensuring safety and effectiveness. The use of multiple identifiers like 'elbow', 'hand', 'head', etc., allows the system to monitor joint angles and barbell height ensuring proper execution.

2. Physical Therapy & Rehabilitation: In physical therapy centers the system can be used to monitor patients recovering from injuries. It can ensure that exercises are being performed correctly, reduce the risk of re-injury, and provide valuable data to therapists.

3. Gym Equipment Manufacturers: Companies manufacturing gym equipment like smart benches can integrate this model into their products to provide an interactive and personalized experience to users with automatic detection and feedback on their form.

4. Fitness Trainers & Coaches: The model could be a valuable tool for trainers and coaches, enabling them to monitor and correct their athlete's form in real time or through a review of recorded sessions. The system can act as a second eye, detecting potential issues that may be missed.

5. Research & Sports Science: Researchers in sports science can use the "Bench Press Detection" model to study more about the biomechanics of the exercise, effectiveness of different techniques or impacts on specific muscle groups. The detailed class detection can provide granular data for in-depth analysis.

Abstract Introduction: The Functional Independence Measure is an evaluation instrument that recognizes the functional evolution of the characteristics of physically disabilities and their abilities. Objective: To analyze the motor items of the functional independence level of people with physical disabilities in the municipality of Itajaí, state of Santa Catarina. Method: A cross-sectional quantitative study was carried out with 164 people with physical disabilities who lived in Itajaí/SC. Socioeconomic variables, and type and cause of disability were collected. To analyze the aspects that limit or contribute to functional independence, the Functional Independence Measure scale was applied. Statistical tests were used for comparisons according to the nature of the variables. Results: Most individuals (39%) were 41 to 60 years old; 44.5% had elementary education; only 10.4% are in the labor market, and 60.4% earns up to two minimum wages. Regarding the type of disability, 58% of participants presented plegia; 26.2%, paresis; and 15.8%, amputations. The etiology of disability was mainly related to neurological problems (43.3%). In the distribution of the average score of people with physical disabilities, half of the sample had average scores above six, and 67% above five, with significant differences in the mean independence scores according to occupation and type of disability. Conclusion: The results obtained support the decision-making process of physical therapists and health professionals.

Background: Physiotherapy is a kind of science that helps and supports the patient to live a healthy lifestyle. Physiotherapy working in India, the main source of reference is a medical practitioner. Physiotherapy is defined as a health care professionals dealing with human mobility and function maximizing the quality of one’s life and movement strength within the loop of prevention, promotion, treatment/intervention, habilitation, and rehabilitation. Still, there are people who aren’t aware of the kind of treatment it can provide. Hereby, the objective of this study is to know how much aware the medical practitioners are in terms of the importance and need for physiotherapy for the treatment of the patients. Materials and Methods: Apparently, an approved questionnaire was sent through a Google form link to 250 medical practitioners of Uttar Pradesh. 124 responses were received and analyzed. Out of 124, 71 of the respondents were female and 53 were male. All willing medical practitioners from different streams along with graduates and super specialists were included, whereas students and non-internet users were excluded. Result: From the study, it was learned that there is awareness regarding the term physiotherapy (), but specialization in physiotherapy is less known, maximum of the subjects were aware of specialization in orthopedics and specialization in women’s health, community-based rehabilitation and dermatology is least known. 79% of the medical practitioners have an objection in physiotherapist having the first contact with the patient. Conclusion: The study revealed that there is a lack of awareness regarding assessment and treatment protocol provided by physiotherapy. However, doctors believe physiotherapist has a big role in treating ICU and immobilized patients. There is less information regarding radiation modalities as well as recent advances in rehabilitation.

Here are a few use cases for this project:

1. Fitness App Development: Developers of fitness or workout apps may utilize this model to track and measure different exercise performances. For example, the model can identify whether an individual is properly lifting a barbell by comparing the individual's form to training data.

2. Equipment Management in Gyms: The model could be used within surveillance cameras to identify how often gym members are utilizing certain equipment like barbells. This could help gym owners better manage and maintain their equipment.

3. Physical Therapy: In a rehabilitation setting, the model could be used to ensure patients are using equipment correctly and safely as they recover from an injury. This could minimize the chance of further harm.

4. Sports Training: Athletes and their coaches might apply the model to analyze and improve lifting techniques in weightlifting or any relevant sports training.

5. Ergonomics Studies: Ergonomics researchers could use this model to study the human body's relationship with different tools such as barbells, placing emphasis on the right posture and handling techniques to enhance efficiency and reduce the risk of injuries.

Results of the multiple regression analysis with simultaneous entry.

Here are a few use cases for this project:

1. Yoga Tutoring Application: This model can be used to power a smart yoga tutoring app that helps users practice proper postures. The application can monitor user's moves in real-time, providing feedback and correction if the pose is not accurately performed.

2. Fitness Gaming: The Yoga Pose Detection model can be used in interactive gaming and virtual reality applications where players mimic certain yoga poses to score points, providing an engaging digital platform for practicing yoga and fitness.

3. Physical Therapy and Rehabilitation: In a health care setting, the model could provide valuable assistance in physical therapy and rehabilitation programs. It can help monitor patient movements, ensuring that exercises are done correctly to prevent injuries and track patient progress.

4. Professional Training for Yoga Teachers: The model can be employed as a training aid for aspiring yoga teachers, helping them to more precisely understand, learn and teach various yoga poses through visual assistance.

5. Scientific Research: Researcher can use this model to study the impact of consistent and proper yoga exercises on the human body. They can utilize this to understand how each pose affects different muscles and body functions.

ObjectivesThe purpose of this study was to engage with physiotherapy clinicians, academics, physiotherapy students and patients to explore the acceptability, feasibility, and practical considerations of implementing person-focused evidence-based pain education concepts, identified from our previous research, in pre-registration physiotherapy training.DesignThis qualitative study took a person-focused approach to ground pain education in the perspectives and experiences of people who deliver and use it. Data was collected via focus groups and in-depth semi-structured interviews. Data was analysed using the seven stage Framework approach.SettingFocus groups and interviews were conducted either face to face, via video conferencing or via telephone. This depended on geographical location, participant preference, and towards the end of data collection the limitations on in-person contact due to the Covid-19 pandemic.ParticipantsUK based physiotherapy clinicians, physiotherapy students, academics and patients living with pain were purposively sampled and invited to take part.ResultsFive focus groups and six semi-structured interviews were conducted with twenty-nine participants. Four key dimensions evolved from the dataset that encapsulate concepts underpinning the acceptability and feasibility of implementing pain education in pre-registration physiotherapy training. These are (1) make pain education authentic to reflect diverse, real patient scenarios, (2) demonstrate the value that pain education adds, (3) be creative by engaging students with content that requires active participation, (4) openly discuss the challenges and embrace scope of practice.ConclusionsThese key dimensions shift the focus of pain education towards practically engaging content that reflects people experiencing pain from diverse sociocultural backgrounds. This study highlights the need for creativity in curriculum design and the importance of preparing graduates for the challenges that they will face in clinical practice.

Here are a few use cases for this project:

1. Sports Training: This model could be used in sports training simulations to identify and track balls in different sports like baseball, football, or soccer. It can help to create efficient training models for players, evaluating their performance in targeting and control over the ball.

2. Gaming Applications: The "Target Game" model could be integrated in video game development where players interact with virtual balls. The model could add an improved level of realism by effectively identifying and responding to ball characteristics.

3. Security Monitoring: The model could be used in public or private spaces, like schoolyards or playgrounds, to monitor ball games and identify potential hazards or rule-breaking.

4. Robotics: In robotics, it can be used to train robots for tasks like sorting or transporting balls of different sizes, colors or materials, hence improving their efficiency and accuracy.

5. Physical Therapy: The model could be used in therapeutic scenarios where patients are asked to interact with balls for coordination or strength exercises. It can track patients' interaction and progression effectively.