Decomposition of Reaching Movements Enables Detection and Measurement of Ataxia

Abstract

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.

Real-life Wrist Movement Patterns Capture Motor Impairment in Individuals with Ataxia-Telangiectasia

“…The shorter submovements had durations that were largely consistent with those reported in the literature during specific motor tasks. These shorter duration submovements also demonstrated an approximately two-thirds power law relationship between distance and velocity (eFigure 5) that has been previously reported [ 14 , 33 ]. The longer submovement group included durations that overlapped with prior work, but also included durations that were longer than those reported (mean 0.2–0.44 s, [ 45 ] mean 0.2–0.6 s, [ 46 ] mean 1.2 s, [ 47 ] range 0.05–2.4 s [ 14 ]).…”

“…Older individuals appear to compensate for greater noise and lower perceptual efficiency by increasing the number of submovements and decreasing the velocity of submovements during accuracy-constrained movement tasks [ 45 ]. During the finger-nose-finger reaching task, individuals with different types of cerebellar ataxia were found to have smaller, shorter, and slower submovements, as well as an increased proportion of submovements with more than one velocity peak [ 14 ]. Consistent with these observations, but in the context of improving motor function, healthy infants’ reaching trajectories become straighter, and movement units decrease in number and increase in duration, with the dominant unit beginning the movement [ 46 ].…”

“…A hallmark characteristic of the ataxia phenotype is that movements are segmented or decomposed into smaller movements [ 21 ] or “submovements.” Movement segmentation may emerge in part from decreased postural tone and support (e.g., against gravity) during voluntary movements [ 21 ], from dyssynchrony of flexor and extensor muscles during coordinated movements [ 22 , 23 ], and/or from a compensatory strategy to decrease postural disturbances [ 24 ]. A recent work demonstrated the utility of quantifying characteristics of wrist submovements in individuals with ataxia during an in-person administered reaching task [ 14 ]; however, it is unknown if clinically useful submovement descriptions can be obtained from natural, at-home behavior.…”

Fine Detection of Human Motion During Activities of Daily Living as a Clinical Indicator for the Detection and Early Treatment of Chronic Diseases: The E-Mob Project

“…Once more, while this remains difficult to track in free-living conditions, the evolution of such movement patterns could be of great interest in the clinical care of these patients. In their work, Oubre et al also underlined that movement decomposition captures the core features of ataxia and may be useful for objective, precise, and frequent assessment of ataxia in home and clinic environments [ 17 ]. While not exhaustive, these examples clearly point out the urgent need to develop new strategies and tools to better track and catch the fine-grained evolution of patients’ body movements in addition to the quantification of habitual PA and sedentary time.…”

Automatic Classification and Severity Estimation of Ataxia From Finger Tapping Videos

“…Previous studies in ataxia have used wearable sensors to quantify rhythmic finger tapping ( 15 ), limb movements such as Finger-to-Nose-Finger and Heel to Shin Test ( 16 – 18 ), free-living movement ( 19 ), and gait ( 20 , 21 ). Other previously used instruments included smartphones to assess fine motor coordination skills ( 22 ) and computer mouse movement during web-based target clicking tasks ( 23 ) and free-viewing web searches ( 24 ) to assess upper-limb movements.…”