Decomposition of Reaching Movements Enables Detection and Measurement of Ataxia

Technologies that enable frequent, objective, and precise measurement of ataxia severity would benefit clinical trials by lowering participation barriers and improving the ability to measure disease state and change. We hypothesized that analyzing characteristics of sub-second movement profiles obtained during a reaching task would be useful for objectively quantifying motor characteristics of ataxia. Participants with ataxia (N=88), participants with parkinsonism (N=44), and healthy controls (N=34) performed a computer tablet version of the finger-to-nose test while wearing inertial sensors on their wrists. Data features designed to capture signs of ataxia were extracted from participants’ decomposed wrist velocity time-series. A machine learning regression model was trained to estimate overall ataxia severity, as measured by the Brief Ataxia Rating Scale (BARS). Classification models were trained to distinguish between ataxia participants and controls and between ataxia and parkinsonism phenotypes. Movement decomposition revealed expected and novel characteristics of the ataxia phenotype. The distance, speed, duration, morphology, and temporal relationships of decomposed movements exhibited strong relationships with disease severity. The regression model estimated BARS with a root mean square error of 3.6 points, r2 = 0.69, and moderate-to-excellent reliability. Classification models distinguished between ataxia participants and controls and ataxia and parkinsonism phenotypes with areas under the receiver-operating curve of 0.96 and 0.89, respectively. Movement decomposition captures core features of ataxia and may be useful for objective, precise, and frequent assessment of ataxia in home and clinic environments

Unobtrusive Assessment of Upper-Limb Motor Impairment Using Wearable Inertial Sensors

Many neurological diseases cause motor impairments that limit autonomy and reduce health-related quality of life. Upper-limb motor impairments, in particular, significantly hamper the performance of essential activities of daily living, such as eating, bathing, and changing clothing. Assessment of impairment is necessary for tracking disease progression, measuring the efficacy of interventions, and informing clinical decision making. Impairment is currently assessed by trained clinicians using semi-quantitative rating scales that are limited by their reliance on subjective, visual assessments. Furthermore, existing scales are often burdensome to administer and do not capture patients\u27 motor performance in home and community settings, resulting in a severely under-sampled view of patients\u27 conditions. Quantitative, objective assessment of upper-limb impairment outside clinical settings could address these limitations, but existing technological solutions generally impose a variety of practical burdens on patients, such as a need to wear many sensors or regularly perform a tightly controlled set of motor tasks. This dissertation first presents data analytic methods that exploit how the central nervous system plans voluntary movements and demonstrates, in controlled settings, that analysis of upper-limb movements can yield information relevant to upper-limb impairment in stroke survivors and patients with ataxia. Fully leveraging these promising findings, this work then further refines and validates these data analytic methods towards the goal of seamless monitoring and assessment of upper-limb function in stroke survivors using only inertial data obtained from patients\u27 natural activities of daily living and a single wrist-worn sensor. This work ultimately aims to support a paradigm shift in how motor impairment is assessed, in which fine-grained and longitudinal tracking of disease progression will enable personalized rehabilitation regimens to optimize therapeutic interventions and promote patient-centric care