Literature DB >> 34913696

Machine-Learning-Aided Self-Powered Assistive Physical Therapy Devices.

Xiao Xiao1, Yunsheng Fang1, Xiao Xiao1, Jing Xu1, Jun Chen1.   

Abstract

An expanding elderly population and people with disabilities pose considerable challenges to the current healthcare system. As a practical technology that integrates systems and services, assistive physical therapy devices are essential to maintain or to improve an individual's functioning and independence, thus promoting their well-being. Given technological advancements, core components of self-powered sensors and optimized machine-learning algorithms will play innovative roles in providing assistive services for unmet global needs. In this Perspective, we provide an overview of the latest developments in machine-learning-aided assistive physical therapy devices based on emerging self-powered sensing systems and a discussion of the challenges and opportunities in this field.

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Mesh:

Year:  2021        PMID: 34913696     DOI: 10.1021/acsnano.1c10676

Source DB:  PubMed          Journal:  ACS Nano        ISSN: 1936-0851            Impact factor:   15.881


  5 in total

1.  Ergonomic Design and Performance Evaluation of H-Suit for Human Walking.

Authors:  Leiyu Zhang; Zhenxing Jiao; Yandong He; Peng Su
Journal:  Micromachines (Basel)       Date:  2022-05-25       Impact factor: 3.523

2.  Trajectory Planning and Simulation Study of Redundant Robotic Arm for Upper Limb Rehabilitation Based on Back Propagation Neural Network and Genetic Algorithm.

Authors:  Xiaohan Qie; Cunfeng Kang; Guanchen Zong; Shujun Chen
Journal:  Sensors (Basel)       Date:  2022-05-27       Impact factor: 3.847

3.  Machine learning approach identified clusters for patients with low cardiac output syndrome and outcomes after cardiac surgery.

Authors:  Xu Zhao; Bowen Gu; Qiuying Li; Jiaxin Li; Weiwei Zeng; Yagang Li; Yanping Guan; Min Huang; Liming Lei; Guoping Zhong
Journal:  Front Cardiovasc Med       Date:  2022-08-18

4.  Debonding-On-Demand Polymeric Wound Patches for Minimal Adhesion and Clinical Communication.

Authors:  Qiankun Zeng; Fangbing Wang; Ruixuan Hu; Xuyin Ding; Yifan Lu; Guoyue Shi; Hossam Haick; Min Zhang
Journal:  Adv Sci (Weinh)       Date:  2022-08-21       Impact factor: 17.521

5.  Using Deep Learning for Task and Tremor Type Classification in People with Parkinson's Disease.

Authors:  Ghazal Farhani; Yue Zhou; Mary E Jenkins; Michael D Naish; Ana Luisa Trejos
Journal:  Sensors (Basel)       Date:  2022-09-27       Impact factor: 3.847

  5 in total

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