Estimation of Gait Parameters in Huntington's Disease Using Wearable Sensors in the Clinic and Free-living Conditions.

In Huntington's disease (HD), wearable inertial sensors could capture subtle changes in motor function. However, disease-specific validation of methods is necessary. This study presents an algorithm for walking bout and gait event detection in HD using a leg-worn accelerometer, validated only in the clinic and deployed in free-living conditions. Seventeen HD participants wore shank- and thigh-worn tri-axial accelerometers, and a wrist-worn device during two-minute walk tests in the clinic, with video reference data for validation. Thirteen participants wore one of the thigh-worn tri-axial accelerometers (AP: ActivPAL4) and the wrist-worn device for 7 days under free-living conditions, with proprietary AP data used as reference. Gait events were detected from shank and thigh acceleration using the Teager-Kaiser energy operator combined with unsupervised clustering. Estimated step count (SC) and temporal gait parameters were compared with reference data. In the clinic, low mean absolute percentage errors were observed for stride (shank/thigh: 0.6/0.9%) and stance (shank/thigh: 3.3/7.1%) times, and SC (shank/thigh: 3.1%). Similar errors were observed for proprietary AP SC (3.2%), with higher errors observed for the wrist-worn device (10.9%). At home, excellent agreement was observed between the proposed algorithm and AP software for SC and time spent walking (ICC [Formula: see text]). The wrist-worn device overestimated SC by 34.2%. The presented algorithm additionally allowed stride and stance time estimation, whose variability correlated significantly with clinical motor scores. The results demonstrate a new method for accurate estimation of HD gait parameters in the clinic and free-living conditions, using a single accelerometer worn on either the thigh or shank.

Language-Independent Acoustic Biomarkers for Quantifying Speech Impairment in Huntington's Disease.

PURPOSE: Changes in voice and speech are characteristic symptoms of Huntington's disease (HD). Objective methods for quantifying speech impairment that can be used across languages could facilitate assessment of disease progression and intervention strategies. The aim of this study was to analyze acoustic features to identify language-independent features that could be used to quantify speech dysfunction in English-, Spanish-, and Polish-speaking participants with HD. METHOD: Ninety participants with HD and 83 control participants performed sustained vowel, syllable repetition, and reading passage tasks recorded with previously validated methods using mobile devices. Language-independent features that differed between HD and controls were identified. Principal component analysis (PCA) and unsupervised clustering were applied to the language-independent features of the HD data set to identify subgroups within the HD data. RESULTS: Forty-six language-independent acoustic features that were significantly different between control participants and participants with HD were identified. Following dimensionality reduction using PCA, four speech clusters were identified in the HD data set. Unified Huntington's Disease Rating Scale (UHDRS) total motor score, total functional capacity, and composite UHDRS were significantly different for pairwise comparisons of subgroups. The percentage of HD participants with higher dysarthria score and disease stage also increased across clusters. CONCLUSION: The results support the application of acoustic features to objectively quantify speech impairment and disease severity in HD in multilanguage studies. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25447171.