清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

Assessment of Motor Impairments in Early Untreated Parkinson's Disease Patients: The Wearable Electronics Impact

可穿戴计算机 物理医学与康复 新颖性 可穿戴技术 医学 运动障碍 疾病 计算机科学 心理学 内科学 嵌入式系统 社会心理学
作者
Mariachiara Ricci,Giulia Di Lazzaro,Antonio Pisani,Nicola Biagio Mercuri,F. Giannini,Giovanni Saggio
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:24 (1): 120-130 被引量:55
标识
DOI:10.1109/jbhi.2019.2903627
摘要

The complex nature of Parkinson's disease (PD) makes difficult to rate its severity, mainly based on the visual inspection of motor impairments. Wearable sensors have been demonstrated to help overcoming such a difficulty, by providing objective measures of motor abnormalities. However, up to now, those sensors have been used on advanced PD patients with evident motor impairment. As a novelty, here we report the impact of wearable sensors in the evaluation of motor abnormalities in newly diagnosed, untreated, namely de novo, patients.A network of wearable sensors was used to measure motor capabilities, in 30 de novo PD patients and 30 healthy subjects, while performing five motor tasks. Measurement data were used to determine motor features useful to highlight impairments and were compared with the corresponding clinical scores. Three classifiers were used to differentiate PD from healthy subjects.Motor features gathered from wearable sensors showed a high degree of significance in discriminating the early untreated de novo PD patients from the healthy subjects, with 95% accuracy. The rates of severity obtained from the measured features are partially in agreement with the clinical scores, with some highlighted, though justified, exceptions.Our findings support the feasibility of adopting wearable sensors in the detection of motor anomalies in early, untreated, PD patients.This work demonstrates that subtle motor impairments, occurring in de novo patients, can be evidenced by means of wearable sensors, providing clinicians with instrumental tools as suitable supports for early diagnosis, and subsequent management.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
ajing发布了新的文献求助10
5秒前
8秒前
8秒前
温暖的芷烟完成签到,获得积分10
8秒前
量子星尘发布了新的文献求助10
9秒前
15秒前
笑点低的斑马完成签到,获得积分10
40秒前
tt完成签到,获得积分10
58秒前
量子星尘发布了新的文献求助10
1分钟前
1分钟前
块块发布了新的文献求助10
1分钟前
鸿俦鹤侣完成签到,获得积分10
1分钟前
科研通AI2S应助科研通管家采纳,获得10
1分钟前
李健的小迷弟应助威菡采纳,获得10
2分钟前
2分钟前
威菡发布了新的文献求助10
2分钟前
chuzihang完成签到 ,获得积分10
2分钟前
xin完成签到,获得积分10
3分钟前
xin发布了新的文献求助10
3分钟前
培培完成签到 ,获得积分10
3分钟前
prawn218完成签到,获得积分10
3分钟前
酷波er应助xin采纳,获得10
3分钟前
3分钟前
Hello应助科研通管家采纳,获得30
3分钟前
科研通AI2S应助科研通管家采纳,获得10
3分钟前
馅饼完成签到,获得积分10
4分钟前
人类后腿完成签到 ,获得积分10
4分钟前
5分钟前
xin发布了新的文献求助10
5分钟前
SUNny发布了新的文献求助10
5分钟前
搬砖的化学男完成签到 ,获得积分10
5分钟前
Panther完成签到,获得积分10
5分钟前
sailingluwl完成签到,获得积分10
5分钟前
科研通AI6应助科研通管家采纳,获得10
5分钟前
科研通AI2S应助科研通管家采纳,获得10
5分钟前
SUNny发布了新的文献求助10
5分钟前
笑傲完成签到,获得积分10
6分钟前
开心每一天完成签到 ,获得积分10
6分钟前
房天川完成签到 ,获得积分10
6分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Binary Alloy Phase Diagrams, 2nd Edition 8000
Building Quantum Computers 800
Translanguaging in Action in English-Medium Classrooms: A Resource Book for Teachers 700
Natural Product Extraction: Principles and Applications 500
Exosomes Pipeline Insight, 2025 500
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5664503
求助须知:如何正确求助?哪些是违规求助? 4863764
关于积分的说明 15107879
捐赠科研通 4823133
什么是DOI,文献DOI怎么找? 2581988
邀请新用户注册赠送积分活动 1536081
关于科研通互助平台的介绍 1494505