Health-Enabling Technologies for Telerehabilitation of the Shoulder: A Feasibility and User Acceptance Study

Steiner, Bianca; Elgert, Lena; Saalfeld, Birgit; Schwartze, Jonas; Borrmann, Horst Peter; Kobelt-Pönicke, Axel; Figlewicz, Andreas; Kasprowski, D.; Thiel, M.; Kreikebohm, Ralf; Haux, Reinhold; Wolf, Klaus-Hendrik

doi:10.1055/s-0040-1713685

Cited by 17 publications

(23 citation statements)

References 20 publications

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“…In many cases, private spaces imply far more than ordinary living. For patients with chronic diseases or patients post-operationally discharged, their homes are places for not only living but also rehabilitation, and—in the future—could also facilitate medical diagnosis and therapy [ 2 , 3 , 4 ]. Health monitoring in private spaces will benefit the subjects who require assistance, such as the elderly, patients, and disabled persons.…”

Section: Introductionmentioning

confidence: 99%

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Unobtrusive Health Monitoring in Private Spaces: The Smart Home

Wang

Spicher

Warnecke

et al. 2021

Sensors

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With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking.

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Section: Introductionmentioning

confidence: 99%

“…In particular, the following questions will be answered: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) Which data can be monitored in the smart home?…”

Section: Introductionmentioning

confidence: 99%

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

Wang

Spicher

Warnecke

et al. 2021

Sensors

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“…It has shown its technical feasibility and user acceptance of HETbased telerehabilitation in a pilot trial. 16 The aim of the described study in this publication is to show the efficacy of AGT-Reha compared with the current standard of care MET.…”

Section: Introductionmentioning

confidence: 99%

“…The prospective, single-arm, monocentric, explorative pilot study AGT-Reha-P2 showed the technical feasibility of AGT-Reha at patients' home. 16 AGT-Reha enables patients to exercise independently at home, while the corresponding data on exercise quality and quantity are automatically forwarded via a web interface to physical therapists. AGT-Reha-P2 also shows the general acceptance by treating physical therapists and patients.…”

Section: Introductionmentioning

confidence: 99%

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AGT-Reha-WK study: protocol for a non-inferiority trial comparing the efficacy and costs of home-based telerehabilitation for shoulder diseases with medical exercise therapy

et al. 2020

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IntroductionShoulder lesions rank among the top 15 diagnoses accounting for days of incapacity to work. Inpatient or full-day outpatient rehabilitation are some of the standard therapies. For sustainable rehabilitation, continuation of rehabilitation after discharge from a rehabilitation centre is vital. Besides medical exercise therapy (MET), home-based physical exercise programmes are used. To monitor exercise quantity and quality, AGT-Reha, a health-enabling technology for home rehabilitation, has been developed and evaluated in a pilot study for technical feasibility and acceptance. To integrate the digital therapeutic AGT-Reha into regular healthcare processes, an efficacy evaluation is required.Methods and analysisAGT-Reha-WK is a prospective, monocentric, non-randomised, unblinded non-inferiority trial. Primary objective is to investigate whether AGT-Reha enhanced home-based exercise training is non-inferior to MET as standard aftercare. Secondary objective is to compare the costs of both therapies. Efficacy as medical success (primary outcome) is examined with regard to ability to work, return to work and sustainability of training (secondary outcomes). The outcome measure for non-inferiority is shoulder function (pain and disability) assessed by the standardised Shoulder Pain and Disability Index (SPADI). The non-inferiority margin is set to 10 points on SPADI score using a 95% CI. Subjects will be recruited at the Rehabilitation Center Bad Pyrmont, Germany. The total number of subjects should be 84 (42 per group). Treatment takes 6 months per patient. Subjects will be assessed at four time points: pre-baseline (admission to rehabilitation centre), baseline (discharge from rehabilitation centre), post-therapy and follow-up (3 months post-therapy).Ethics and disseminationEthics approval was granted by the Ethics Committee of Hannover Medical School (ethics approval no: 7313). Results of the trial are planned to be published in a peer-reviewed journal.Trial registration numberGerman Clinical Trials Register DRKS00011596. Registered 2 June 2017. Recruitment started on 3 March 2017, and it is expected to continue until December 2020.Protocol versionV2.0, 23 May 2018, Amendment 01: improved risk analysis, clarification of exclusion criteria to increase reproducibility, additional documentation with OpenClinica; these changes have no effect on structural equality.

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Machine‐learning models for shoulder rehabilitation exercises classification using a wearable system

Sassi,

Carnevale,

Mancuso

et al. 2024

Knee surg. sports traumatol. arthrosc.

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PurposeThe objective of this study is to train and test machine‐learning (ML) models to automatically classify shoulder rehabilitation exercises.MethodsThe cohort included both healthy and patients with rotator‐cuff (RC) tears. All participants performed six shoulder rehabilitation exercises, following guidelines developed by the American Society of Shoulder and Elbow Therapists. Each exercise was repeated six times, while wearing a wearable system equipped with three magneto‐inertial sensors. Six supervised machine‐learning models (k‐Nearest Neighbours, Support Vector Machine, Decision Tree, Random Forest (RF), Logistic Regression and Adaptive Boosting) were trained for the classification. The algorithms' ability to accurately classify exercise activities was evaluated using the nested cross‐validation method, with different combinations of outer and inner folds.ResultsA total of 19 healthy subjects and 17 patients with complete RC tears were enroled in the study. The highest classification performances were achieved by the RF classifier, with an accuracy of 89.91% and an F1‐score of 89.89%.ConclusionThe results of this study highlight the feasibility and effectiveness of using wearable sensors and ML algorithms to accurately classify shoulder rehabilitation exercises. These findings suggest promising prospects for implementing the proposed wearable system in remote home‐based monitoring scenarios. The ease of setup and modularity of the system reduce user burden enabling patient‐driven sensor positioning.Level of EvidenceLevel III.

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Health-Enabling Technologies for Telerehabilitation of the Shoulder: A Feasibility and User Acceptance Study

Cited by 17 publications

References 20 publications

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

AGT-Reha-WK study: protocol for a non-inferiority trial comparing the efficacy and costs of home-based telerehabilitation for shoulder diseases with medical exercise therapy

Machine‐learning models for shoulder rehabilitation exercises classification using a wearable system

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