Artificial intelligence technologies are considered crucial in supporting a decentralized model of care in which therapeutic interventions are provided from a distance. In the last years, various approaches have been proposed to support remote monitoring and smart assistance in rehabilitation services. A comprehensive state-of-the-art of machine learning methods and applications is presented in this review. Following PRISMA guidelines, a systematic literature search strategy was led in PubMed, Scopus, and IEEE Xplore databases. The search yielded 519 records, resulting in 35 articles included in this study. Supervised and unsupervised machine learning algorithms were identified. Unobtrusive capture motion technologies have been identified as strategic applications to support remote and smart monitoring. The most addressed tasks by algorithms were activity recognition, movement classification, and clinical status prediction. Some authors evidenced drawbacks concerning the low generalizability of the results retrieved. Artificial intelligence-based applications are likely to impact the delivery of decentralized rehabilitation services by providing broad access to sustained and high-quality therapy. Future efforts are needed to validate artificial intelligence technologies in specific clinical populations and evaluate results reliability in remote conditions and home-based settings. INDEX TERMS Digital therapeutics, e-health, remote monitoring, intelligent systems, deep learning, machine learning.
Technology-aided hand functional assessment has received considerable attention in recent years. Its applications are required to obtain objective, reliable, and sensitive methods for clinical decision making. This systematic review aims to investigate and discuss characteristics of technology-aided hand functional assessment and their applications, in terms of the adopted sensing technology, evaluation methods and purposes. Based on the shortcomings of current applications, and opportunities offered by emerging systems, this review aims to support the design and the translation to clinical practice of technology-aided hand functional assessment. To this end, a systematic literature search was led, according to recommended PRISMA guidelines, in PubMed and IEEE Xplore databases. The search yielded 208 records, resulting into 23 articles included in the study. Glove-based systems, instrumented objects and body-networked sensor systems appeared from the search, together with vision-based motion capture systems, end-effector, and exoskeleton systems. Inertial measurement unit (IMU) and force sensing resistor (FSR) resulted the sensing technologies most used for kinematic and kinetic analysis. A lack of standardization in system metrics and assessment methods emerged. Future studies that pertinently discuss the pathophysiological content and clinimetrics properties of new systems are required for leading technologies to clinical acceptance.
Background: Carpal tunnel syndrome (CTS) compromises fine sensorimotor function during activities of daily living and affects a large number of individuals with high burden costs for society. The purpose of this study was to quantitatively characterize fine movement skills in CTS patients preoperatively and at 1 month postoperatively by means of a sensor-engineered glove, in order to provide new insights for evaluative and finally therapeutic purposes. Methods: Forty-one CTS patients and 41 age- and gender-matched healthy controls (HC) were analyzed by adopting the engineered glove Hand Test System (HTS), which previously demonstrated its reliability and sensitivity to detect hands dysfunction in several neurological diseases. A sub-group of 11 CTS subjects was re-tested 1 month after surgery. Three parameters—touch duration (TD), inter-tapping interval (ITI), and movement rate (MR)—were considered to characterize hand function. Results: The affected hand of CTS patients generally showed worst finger opposition performances than HC. Comparing the dominant hand, all parameters were able to significantly discriminate CTS patients from HC. Considering the nondominant hand, the best performing parameter in discriminating CTS from HC was TD. The follow-up assessment at 1 month after surgery showed that considered parameters were able to monitor patients’ recovery. In particular, the TD parameter recorded at the 3 different assigned task modalities resulted significantly enhanced. Conclusions: Results of this pilot study proved the validity of the parameters obtained through the sensor-engineered glove to assess objectively hand functional status and surgical outcomes in CTS.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.