Quantitative Assessment of the Arm/Hand Movements in Parkinson’s Disease Using a Wireless Armband Device

Abstract

We present an approach for quantitative assessment of the arm/hand movements in patients with Parkinson’s disease (PD), from sensor data acquired with a wearable, wireless armband device (Myo sensor). We propose new Movement Performance Indicators that can be adopted by practitioners for the quantitative evaluation of motor performance and support their clinical evaluations. In addition, specific Movement Performance Indicators can indicate the presence of the bradykinesia symptom. The study includes seventeen PD patients and sixteen age-matched controls. A set of representative arm/hand movements is defined under the supervision of movement disorder specialist. In order to assist the evaluations, and for progress monitoring purposes, as well as for assessing the amount of bradykinesia in PD, a total set of 84 Movement Performance Indicators are computed from the sensor readings. Subsequently, we investigate whether wireless armband device, with the use of the proposed Movement Performance Indicators can be utilized: (1) for objective and precise quantitative evaluation of the arm/hand movements of Parkinson’s patients, (2) for assessment of the bradykinesia motor symptom, and (3) as an adequate low-cost alternative for the sensor glove. We conducted extensive analysis of proposed Movement Performance Indicators and results are indicating following clinically relevant characteristics: (i) adequate reliability as measured by ICC; (ii) high accuracy in discrimination between the patients and controls, and between the disease stages (support to disease diagnosis and progress monitoring, respectively); (iii) substantial difference in comparison between the left-hand and the right-hand movements across controls and patients, as well as between disease stage groups; (iv) statistically significant correlation with clinical scales (tapping test and UPDRS-III Motor Score); and (v) quantitative evaluation of bradykinesia symptom. Results suggest that the proposed approach has a potential to be adopted by physicians, to afford them with quantitative, objective and precise methods and data during clinical evaluations and support the assessment of bradykinesia.

Objective assessment of bradykinesia in Parkinson’s disease using evolutionary algorithms: clinical validation

“…Other research groups have also explored computer-based systems and wearable sensors to quantitatively assess parkinsonism related symptoms including bradykinesia [23–28] and dyskinesia [29, 30]. Different tasks were used to assess bradykinesia such as FT [27], hand movements [28], toe tapping and leg agility [29]. In general, most of them only focus on gross movement features, such as mean amplitude and velocity, and construct the classifier through linear combinations of those features [26].…”

Acceptability to patients, carers and clinicians of an mHealth platform for the management of Parkinson’s disease (PD_Manager): study protocol for a pilot randomised controlled trial

“…This mHealth platform aims to provide a continuous feed of data on symptoms to improve clinical understanding of the fluctuating status of any individual patient and to inform care planning in which different experts may be involved, as well as prescribing. Continuous quantitative monitoring of activities and medication-induced fluctuations using wearable devices has been found feasible and useful in prior studies [ 4 , 5 ], including exercise interventions [ 6 ]. Research has also focussed on finding the optimal location for monitoring motor performance [ 7 ].…”

Parkinson’s disease medication state and severity assessment based on coordination during walking

“…Better correlations were found when the metrics were compared with UPDRS motor subscores for gait and balance such as angular velocity [ 32 ] in both early PD (r 2 = 0.17, p < 0.01) and PD + freeze of gait participants (r 2 = 0.61, p < 0.01). For EMG data, Spasojević et al [ 14 ] obtaining significant correlations between their estimated features and UPDRS scores (r 2 > 0.25) by analyzing the movement of the arm/hand in 17 PD participants. In a study more aligned with ours, Huang et al [ 11 ] explored body coordination of the arms in 8 PD participants while walking.…”

“…Typically, due to various PD related impairments, posture and arm swing become asymmetric [ 8 , 11 – 13 ]. Different laboratories have assessed and quantified these impairments using high definition motion capture analysis and electromyographic recordings [ 9 , 14 ]. These methods are restricted to complex and expensive research laboratories settings, which makes them unsuitable to monitor participants outside the clinic and to understand the effects of medication and interventions on the progression of the disease [ 4 , 5 ] in a more typical setting [ 15 ].…”