Monitoring rehabilitation exercises using MS Kinect

Anton, Silvia; Anton, Florin Daniel

doi:10.1109/ehb.2015.7391463

Cited by 8 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The types of feedback provided to patients are described in the next section. Human performance data (n=25) are those used to indicate movement or exercise performance of the individual [ 22 , 36 - 40 , 42 , 45 - 48 , 52 , 53 , 57 , 59 , 60 , 62 , 64 - 67 , 69 - 72 ]. Most of these data were generated directly from the Kinect sensor; others were from different sensors such as accelerometers.…”

Section: Resultsmentioning

confidence: 99%

“…Guidance feedback (n=19) is in the form of visual and auditory information, intended to facilitate performing an exercise or task. In 13 papers, patients were guided in performing a task through a visual interface [ 44 , 45 , 49 , 52 , 53 , 60 - 62 , 64 , 67 , 70 - 72 ]; in 1 paper, through auditory feedback [ 30 ]; and in 5 papers, through both visual and auditory guidance [ 23 , 35 , 54 , 56 , 66 ]. Progress feedback (n=4) tracked patient progress in terms of number of exercises or tasks completed or to be completed [ 42 , 46 , 63 , 65 ].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Dimaguila

Gray

Merolli

2018

JMIR Rehabil Assist Technol

View full text Add to dashboard Cite

BackgroundPerson- or patient-generated health data (PGHD) are health, wellness, and clinical data that people generate, record, and analyze for themselves. There is potential for PGHD to improve the efficiency and effectiveness of simulated rehabilitation technologies for stroke. Simulated rehabilitation is a type of telerehabilitation that uses computer technologies and interfaces to allow the real-time simulation of rehabilitation activities or a rehabilitation environment. A leading technology for simulated rehabilitation is Microsoft’s Kinect, a video-based technology that uses infrared to track a user’s body movements.ObjectiveThis review attempts to understand to what extent Kinect-based stroke rehabilitation systems (K-SRS) have used PGHD and to what benefit.MethodsThe review is conducted in two parts. In part 1, aspects of relevance for PGHD were searched for in existing systematic reviews on K-SRS. The following databases were searched: IEEE Xplore, Association of Computing Machinery Digital Library, PubMed, Biomed Central, Cochrane Library, and Campbell Collaboration. In part 2, original research papers that presented or used K-SRS were reviewed in terms of (1) types of PGHD, (2) patient access to PGHD, (3) PGHD use, and (4) effects of PGHD use. The search was conducted in the same databases as part 1 except Cochrane and Campbell Collaboration. Reference lists on K-SRS of the reviews found in part 1 were also included in the search for part 2. There was no date restriction. The search was closed in June 2017. The quality of the papers was not assessed, as it was not deemed critical to understanding PGHD access and use in studies that used K-SRS.ResultsIn part 1, 192 papers were identified, and after assessment only 3 papers were included. Part 1 showed that previous reviews focused on technical effectiveness of K-SRS with some attention on clinical effectiveness. None of those reviews reported on home-based implementation or PGHD use. In part 2, 163 papers were identified and after assessment, 41 papers were included. Part 2 showed that there is a gap in understanding how PGHD use may affect patients using K-SRS and a lack of patient participation in the design of such systems.ConclusionsThis paper calls specifically for further studies of K-SRS—and for studies of technologies that allow patients to generate their own health data in general—to pay more attention to how patients’ own use of their data may influence their care processes and outcomes. Future studies that trial the effectiveness of K-SRS outside the clinic should also explore how patients and carers use PGHD in home rehabilitation programs.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Dimaguila

Gray

Merolli

2018

JMIR Rehabil Assist Technol

View full text Add to dashboard Cite

show abstract

“…Anton et al [9], propose a system for monitoring rehabilitation exercises to track patient progress, without the need for a specialist. The system can be trained to detect exercise and compare movement with the correct form, providing feedback to the patient and tracking progress.…”

Section: State-of-the-artmentioning

confidence: 99%

“…In addition, errors based on Kinect's schematic error may occur for the detected attachment points. Another disadvantage of the system is that it only considers the angles of the segment of the skeleton in two dimensions when projecting the segments in the XY plane, without taking into account the perspective [9]. An evaluation with Kinect v2 may yield greater results.…”

Section: State-of-the-artmentioning

confidence: 99%

“…In this paper we applied HMM described in [4] by Utarwar et al, and HCRF in [10] by Wang et al Using the position descriptor based on points [5] and the position descriptor based on angles by Anton et al [9], considering three dimensions XYZ, described in [7] by Vemulapalli et al It was therefore necessary to assess whether the position descriptor based on angles is as significant as the position descriptor based on points with two different HMM and HCRF algorithms.…”

Section: State-of-the-artmentioning

confidence: 99%

See 1 more Smart Citation

Virtual Rehabilitation Using Sequential Learning Algorithms

Condori

Gutierrez²,

Casas³

2018

ijacsa

View full text Add to dashboard Cite

Rehabilitation systems are becoming more important now because patients can access motor skills recovery treatment from home, reducing the limitations of time, space and cost of treatment in a medical facility. Traditional rehabilitation systems served as movement guides, later as movement mirrors, and in recent years research has sought to generate feedback messages to the patient based on the evaluation of his or her movements. Currently the most commonly used algorithms for exercise evaluation are Dynamic time warping (DTW), Hidden Markov model (HMM), Support vector machine (SVM). However, the larger the set of exercises to be evaluated, the less accurate the recognition becomes, generating confusion between exercises that have similar posture descriptors. This research paper compares two HMM classifiers and Hidden Conditional Random Fields (HCRF) plus two types of posture descriptors, based on points and based on angles. Point representation proves to be superior to angle representation, although the latter is still acceptable. Similar results are found in HCRF and HMM.

show abstract

Adherence Tracking With Smart Watches for Shoulder Physiotherapy in Rotator Cuff Pathology: Protocol for a Longitudinal Cohort Study

Burns¹,

Razmjou²,

Shaw³

et al. 2020

JMIR Res Protoc

View full text Add to dashboard Cite

Background Physiotherapy is essential for the successful rehabilitation of common shoulder injuries and following shoulder surgery. Patients may receive some training and supervision for shoulder physiotherapy through private pay or private insurance, but they are typically responsible for performing most of their physiotherapy independently at home. It is unknown how often patients perform their home exercises and if these exercises are performed correctly without supervision. There are no established tools for measuring this. It is, therefore, unclear if the full benefit of shoulder physiotherapy treatments is being realized. Objective The proposed research will (1) validate a smartwatch and machine learning (ML) approach for evaluating adherence to shoulder exercise participation and technique in a clinical patient population with rotator cuff pathology; (2) quantify the rate of home physiotherapy adherence, determine the effects of adherence on recovery, and identify barriers to successful adherence; and (3) develop and pilot test an ethically conscious adherence-driven rehabilitation program that individualizes patient care based on their capacity to effectively participate in their home physiotherapy. Methods This research will be conducted in 2 phases. The first phase is a prospective longitudinal cohort study, involving 120 patients undergoing physiotherapy for rotator cuff pathology. Patients will be issued a smartwatch that will record 9-axis inertial sensor data while they perform physiotherapy exercises both in the clinic and in the home setting. The data collected in the clinic under supervision will be used to train and validate our ML algorithms that classify shoulder physiotherapy exercise. The validated algorithms will then be used to assess home physiotherapy adherence from the inertial data collected at home. Validated outcome measures, including the Disabilities of the Arm, Shoulder, and Hand questionnaire; Numeric Pain Rating Scale; range of motion; shoulder strength; and work status, will be collected pretreatment, monthly through treatment, and at a final follow-up of 12 months. We will then relate improvement in patient outcomes to measured physiotherapy adherence and patient baseline variables in univariate and multivariate analyses. The second phase of this research will involve the evaluation of a novel rehabilitation program in a cohort of 20 patients. The program will promote patient physiotherapy engagement via the developed technology and support adherence-driven care decisions. Results As of December 2019, 71 patients were screened for enrollment in the noninterventional validation phase of this study; 65 patients met the inclusion and exclusion criteria. Of these, 46 patients consented and 19 declined to participate in the study. Only 2 patients de-enrolled from the study and data collection is ongoing for the remaining 44. Conclusions This study wil...

show abstract

Monitoring rehabilitation exercises using MS Kinect

Cited by 8 publications

References 10 publications

Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Virtual Rehabilitation Using Sequential Learning Algorithms

Adherence Tracking With Smart Watches for Shoulder Physiotherapy in Rotator Cuff Pathology: Protocol for a Longitudinal Cohort Study

Contact Info

Product

Resources

About