A Wearable Solution of Muscle Atrophy Assessment: Oriented Toward Upper Limb Rehabilitation

被引:0
|
作者
Wang, Qin [1 ]
Wang, Daomiao [1 ]
Yang, Cuiwei [1 ,2 ]
Huang, Xiaonan [3 ]
Fang, Fanfu [3 ]
Song, Zilong [1 ]
Xiang, Wei [1 ]
机构
[1] Fudan Univ, Sch Informat Sci & Technol, Dept Biomed Engn, Shanghai 200433, Peoples R China
[2] Shanghai Inst Intelligent Elect & Syst, Shanghai 200433, Peoples R China
[3] Naval Med Univ, Changhai Hosp, Shanghai 200433, Peoples R China
关键词
bioimpedance; upper limb rehabilitation; muscle atrophy; machine learning; wearable device; ELECTRICAL-IMPEDANCE; SEVERITY;
D O I
10.3390/electronics13204138
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the process of the upper limb rehabilitation, the rehabilitation effect is often evaluated from the perspective of the motor function of limbs. However, the state of muscle atrophy is also a noteworthy indicator reflecting the rehabilitation effect. We proposed a wearable solution for the monitoring and grade assessing of local muscle atrophy based on wearable bioimpedance (BioZ) sensors. This work elaborates on the theoretical basis, procedure, and key influencing factors of the proposed solution, and the feasibility and effectiveness have been verified through in vitro and in vivo experiments. A total of 25 phantoms in different CSA (cross-sectional area) and FMR (fat-to-muscle ratio) values were designed to simulate different stages of muscular atrophy, and a linear correlation was observed between BioZ, CSA, and FMR, with an R-squared value of 0.898. The relative impedance difference of 38 patients with unilateral muscle atrophy was 5.231% larger than that of 30 healthy control samples on average (p < 0.05). These results demonstrate the correlation between muscle atrophy and BioZ. As the proof-of-concept for graded assessment, the results analyzed by support vector machines (SVMs) show that the accuracy of three-level classification can reach 94.1% using the five-fold cross-validation.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] Pneumatic Muscle Actuated Rehabilitation Equipment of the Upper Limb Joints
    Deaconescu, Andrea
    4TH INTERNATIONAL CONFERENCE ON MANUFACTURING AND INDUSTRIAL TECHNOLOGIES (ICMIT 2017), 2017, 212
  • [22] ArmAssist: A Telerehabilitation Solution for Upper-Limb Rehabilitation at Home
    Garzo, Ainara
    Jung, Je H.
    Arcas-Ruiz-Ruano, Javier
    Perry, Joel C.
    Keller, Thierry
    IEEE ROBOTICS & AUTOMATION MAGAZINE, 2023, 30 (01) : 62 - 71
  • [23] Workspace Analysis of Upper Limb for a Planar Cable-Driven Parallel Robots toward Upper Limb Rehabilitation
    Jin, XueJun
    Jun, Dae Ik
    Jin, Xuemei
    Park, Sukho
    Park, Jong-Oh
    Ko, Seong Young
    2014 14TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2014), 2014, : 352 - 356
  • [24] Toward Upper-Body Posture Monitoring for Upper-Limb Rehabilitation Robot
    Hwang, Yeji
    Kim, Jonghyun
    INTELLIGENT AUTONOMOUS SYSTEMS 18, VOL 1, IAS18-2023, 2024, 795 : 621 - 628
  • [25] Analysis of upper limb motions in tennis swings toward rehabilitation training
    Wada, T
    Yoshii, N
    Yamaji, Y
    Tanaka, S
    Tsukamoto, K
    2002 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-3, PROCEEDINGS, 2002, : 1397 - 1402
  • [26] Upper limb intention tremor assessment: opportunities and challenges in wearable technology
    Natalia Paredes-Acuna
    Daniel Utpadel-Fischler
    Keqin Ding
    Nitish V. Thakor
    Gordon Cheng
    Journal of NeuroEngineering and Rehabilitation, 21
  • [27] Upper limb intention tremor assessment: opportunities and challenges in wearable technology
    Paredes-Acuna, Natalia
    Utpadel-Fischler, Daniel
    Ding, Keqin
    Thakor, Nitish V.
    Cheng, Gordon
    JOURNAL OF NEUROENGINEERING AND REHABILITATION, 2024, 21 (01)
  • [28] Quantitative Upper Limb Impairment Assessment for Stroke Rehabilitation: A Review
    Wang, Xin
    Zhang, Jie
    Xie, Sheng Quan
    Shi, Chaoyang
    Li, Jun
    Zhang, Zhi-Qiang
    IEEE SENSORS JOURNAL, 2024, 24 (06) : 7432 - 7447
  • [29] Objective Assessment of Upper-Limb Mobility for Poststroke Rehabilitation
    Zhang, Zhe
    Fang, Qiang
    Gu, Xudong
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2016, 63 (04) : 859 - 868
  • [30] Dexterous Haptic Interaction for Functional Rehabilitation and Assessment of the Upper Limb
    Yu, Ningbo
    Wang, Kui
    Liu, Jingtai
    2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS IEEE-ROBIO 2014, 2014, : 1351 - 1355