Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

Regterschot, G. Ruben H.; Ribbers, Gerard M.; Bussmann, Johannes B. J.

doi:10.3390/s21144744

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…Besides research into wearable use in stride, step, stance, and spatiotemporal variables relative to both performance and injury mitigation, a greater understanding of the processes and predictors of SIJ rehabilitation has the potential to inform and strengthen public health. In this regard, the findings agree with Regterschot et al [ 67 ] in that important challenges and barriers to the deployment of wearables in clinical care remain. Similarly, Lang et al [ 68 ] discussed the major barriers to the application of wearables in motor rehabilitation and proposed benchmarks for the implementation of wearables in clinical practice.…”

Section: Discussionsupporting

confidence: 90%

“…This extends to the development and optimization of innovative wearable configurations and data analysis techniques (eg, machine learning–based algorithms that enable the detection of specific activities and movements in free-living conditions). While Regterschot et al [ 67 ] asserted the existence of reliable and valid wearables for clinical populations and free-living environments, medical technology professionals could be encouraged to assist allied health specialists in developing the knowledge and skills necessary to effectively use wearables for remote rehabilitation purposes. In concordance with Regterschot et al [ 67 ], barriers exist in deploying remote wearables for detecting specific activities and movements in free-living conditions.…”

Section: Discussionmentioning

confidence: 99%

“…While Regterschot et al [ 67 ] asserted the existence of reliable and valid wearables for clinical populations and free-living environments, medical technology professionals could be encouraged to assist allied health specialists in developing the knowledge and skills necessary to effectively use wearables for remote rehabilitation purposes. In concordance with Regterschot et al [ 67 ], barriers exist in deploying remote wearables for detecting specific activities and movements in free-living conditions. The results of this review suggest that clinical barriers extend to the busy medical environment and the lack of realization of the value of information that can be obtained using wearables.…”

Section: Discussionmentioning

confidence: 99%

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Sacroiliac Joint Dysfunction in Endurance Runners Using Wearable Technology as a Clinical Monitoring Tool: Systematic Review

Evans

2024

JMIR Biomed Eng

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Background In recent years, researchers have delved into the relationship between the anatomy and biomechanics of sacroiliac joint (SIJ) pain and dysfunction in endurance runners to elucidate the connection between lower back pain and the SIJ. However, the majority of SIJ pain and dysfunction cases are diagnosed and managed through a traditional athlete-clinician arrangement, where the athlete must attend regular in-person clinical appointments with various allied health professionals. Wearable sensors (wearables) are increasingly serving as a clinical diagnostic tool to monitor an athlete’s day-to-day activities remotely, thus eliminating the necessity for in-person appointments. Nevertheless, the extent to which wearables are used in a remote setting to manage SIJ dysfunction in endurance runners remains uncertain. Objective This study aims to conduct a systematic review of the literature to enhance our understanding regarding the use of wearables in both in-person and remote settings for biomechanical-based rehabilitation in SIJ dysfunction among endurance runners. In addressing this issue, the overarching goal was to explore how wearables can contribute to the clinical diagnosis (before, during, and after) of SIJ dysfunction. Methods Three online databases, including PubMed, Scopus, and Google Scholar, were searched using various combinations of keywords. Initially, a total of 4097 articles were identified. After removing duplicates and screening articles based on inclusion and exclusion criteria, 45 articles were analyzed. Subsequently, 21 articles were included in this study. The quality of the investigation was assessed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) evidence-based minimum set of items for reporting in systematic reviews. Results Among the 21 studies included in this review, more than half of the investigations were literature reviews focusing on wearable sensors in the diagnosis and treatment of SIJ pain, wearable movement sensors for rehabilitation, or a combination of both for SIJ gait analysis in an intelligent health care setting. As many as 4 (19%) studies were case reports, and only 1 study could be classified as fully experimental. One paper was classified as being at the “pre” stage of SIJ dysfunction, while 6 (29%) were identified as being at the “at” stage of classification. Significantly fewer studies attempted to capture or classify actual SIJ injuries, and no study directly addressed the injury recovery stage. Conclusions SIJ dysfunction remains underdiagnosed and undertreated in endurance runners. Moreover, there is a lack of clear diagnostic or treatment pathways using wearables remotely, despite the availability of validated technology. Further research of higher quality is recommended to investigate SIJ dysfunction in endurance runners and explore the use of wearables for rehabilitation in remote settings.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Sacroiliac Joint Dysfunction in Endurance Runners Using Wearable Technology as a Clinical Monitoring Tool: Systematic Review

Evans

2024

JMIR Biomed Eng

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“…Historically, these measures would be limited to laboratory environments with sophisticated and expensive equipment. However, the evolution and widespread adoption of wearable sensors continues to break down barriers to clinical translation [ 87 , 88 ]—increasing the feasibility of these measures in the clinic. Ultimately, studies into the mechanistic causal associations between muscle-level changes and gait performance outcomes are warranted into order to identify potentially modifiable factors and therapeutic interventions.…”

Section: Gaps In the Sciencementioning

confidence: 99%

Multimodal Diagnostic Approaches to Advance Precision Medicine in Sarcopenia and Frailty

et al. 2022

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Sarcopenia, defined as the loss of muscle mass, strength, and function with aging, is a geriatric syndrome with important implications for patients and healthcare systems. Sarcopenia increases the risk of clinical decompensation when faced with physiological stressors and increases vulnerability, termed frailty. Sarcopenia develops due to inflammatory, hormonal, and myocellular changes in response to physiological and pathological aging, which promote progressive gains in fat mass and loss of lean mass and muscle strength. Progression of these pathophysiological changes can lead to sarcopenic obesity and physical frailty. These syndromes independently increase the risk of adverse patient outcomes including hospitalizations, long-term care placement, mortality, and decreased quality of life. This risk increases substantially when these syndromes co-exist. While there is evidence suggesting that the progression of sarcopenia, sarcopenic obesity, and frailty can be slowed or reversed, the adoption of broad-based screening or interventions has been slow to implement. Factors contributing to slow implementation include the lack of cost-effective, timely bedside diagnostics and interventions that target fundamental biological processes. This paper describes how clinical, radiographic, and biological data can be used to evaluate older adults with sarcopenia and sarcopenic obesity and to further the understanding of the mechanisms leading to declines in physical function and frailty.

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“…Various measures have been developed to quantify upper limb use from only wrist-worn IMU data ( Bailey et al, 2014 ), ( Lum et al, 2020 ), ( Uswatte et al, 2000 ), ( Leuenberger et al, 2017 ), ( De Lucena et al, 2017 ). Measures to quantify upper limb use using additional inertial measurements from other body segments exist in the literature ( Uswatte et al, 2000 ), ( Regterschot et al, 2021 ) but this study restricts its attention to only wrist-worn inertial measurements due to its popularity and practicality. The measures for detecting upper limb use employing only wrist-worn IMU data can be grouped into three types: 1) Thresholded activity counting ( Bailey et al, 2014 ), ( Uswatte et al, 2000 ), ( De Lucena et al, 2017 ), 2) Gross movement score ( Leuenberger et al, 2017 ), and 3) machine learning ( Lum et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Comparing algorithms for assessing upper limb use with inertial measurement units

et al. 2022

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The various existing measures to quantify upper limb use from wrist-worn inertial measurement units can be grouped into three categories: 1) Thresholded activity counting, 2) Gross movement score and 3) machine learning. However, there is currently no direct comparison of all these measures on a single dataset. While machine learning is a promising approach to detecting upper limb use, there is currently no knowledge of the information used by machine learning measures and the data-related factors that influence their performance. The current study conducted a direct comparison of the 1) thresholded activity counting measures, 2) gross movement score,3) a hybrid activity counting and gross movement score measure (introduced in this study), and 4) machine learning measures for detecting upper-limb use, using previously collected data. Two additional analyses were also performed to understand the nature of the information used by machine learning measures and the influence of data on the performance of machine learning measures. The intra-subject random forest machine learning measure detected upper limb use more accurately than all other measures, confirming previous observations in the literature. Among the non-machine learning (or traditional) algorithms, the hybrid activity counting and gross movement score measure performed better than the other measures. Further analysis of the random forest measure revealed that this measure used information about the forearm’s orientation and amount of movement to detect upper limb use. The performance of machine learning measures was influenced by the types of movements and the proportion of functional data in the training/testing datasets. The study outcomes show that machine learning measures perform better than traditional measures and shed some light on how these methods detect upper-limb use. However, in the absence of annotated data for training machine learning measures, the hybrid activity counting and gross movement score measure presents a reasonable alternative. We believe this paper presents a step towards understanding and optimizing measures for upper limb use assessment using wearable sensors.

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Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

Abstract: Motor disorders are a common and age-related problem in the general community [...]

Cited by 11 publications

References 18 publications

Sacroiliac Joint Dysfunction in Endurance Runners Using Wearable Technology as a Clinical Monitoring Tool: Systematic Review

Sacroiliac Joint Dysfunction in Endurance Runners Using Wearable Technology as a Clinical Monitoring Tool: Systematic Review

Multimodal Diagnostic Approaches to Advance Precision Medicine in Sarcopenia and Frailty

Comparing algorithms for assessing upper limb use with inertial measurement units

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