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.
最长约 10秒,即可获得该文献文件

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
周周完成签到,获得积分10
1秒前
inin完成签到 ,获得积分10
1秒前
陶醉的啤酒完成签到 ,获得积分10
2秒前
4秒前
tony完成签到,获得积分10
4秒前
ghhhn完成签到,获得积分10
6秒前
奋斗的俊驰完成签到,获得积分10
6秒前
滴水拨纹完成签到,获得积分10
7秒前
Morssax发布了新的文献求助10
8秒前
闫雨完成签到 ,获得积分10
9秒前
所所应助ycy采纳,获得10
9秒前
Zhou完成签到,获得积分10
11秒前
12秒前
可爱的函函应助行7采纳,获得10
13秒前
雪影完成签到 ,获得积分10
13秒前
完美世界应助can采纳,获得10
15秒前
夏至发布了新的文献求助10
17秒前
18秒前
19秒前
19秒前
19秒前
19秒前
mengtingmei应助科研通管家采纳,获得10
19秒前
wabfye应助科研通管家采纳,获得10
19秒前
wabfye应助科研通管家采纳,获得10
19秒前
bkagyin应助科研通管家采纳,获得10
19秒前
20秒前
乐乐应助科研通管家采纳,获得10
20秒前
July应助科研通管家采纳,获得10
20秒前
wabfye应助科研通管家采纳,获得10
20秒前
无限芷波发布了新的文献求助10
20秒前
20秒前
mengtingmei应助科研通管家采纳,获得10
20秒前
mio完成签到,获得积分20
20秒前
完美世界应助hyodong采纳,获得10
21秒前
zzz完成签到,获得积分10
24秒前
799完成签到 ,获得积分10
24秒前
柳柳发布了新的文献求助10
25秒前
威武的绿兰完成签到,获得积分10
26秒前
星辰大海应助CANG采纳,获得10
26秒前
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
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
Russian Politics Today: Stability and Fragility (2nd Edition) 500
Death Without End: Korea and the Thanatographics of War 500
Der Gleislage auf der Spur 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6082300
求助须知:如何正确求助?哪些是违规求助? 7912725
关于积分的说明 16364770
捐赠科研通 5217710
什么是DOI,文献DOI怎么找? 2789558
邀请新用户注册赠送积分活动 1772554
关于科研通互助平台的介绍 1649138