Predicting Severity of Huntington's Disease With Wearable Sensors

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.

Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review

This article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field.

Measurement of bradykinesia and chorea in Huntington's Disease using ambulatory monitoring

Objectives

The feasibility of measuring bradykinesia and chorea in Huntington's Disease using a wearable sensor system (Parkinson's Kinetigraph: PKG) developed for measuring bradykinesia and dyskinesia in Parkinson's Disease was assessed.

Methods

Unified Huntington's Disease Rating Scales (UHDRS) and a PKG were obtained for 25 people with Huntington's Disease. Bradykinesia and Chorea Score were derived from relevant sub-scores of the UHDRS and compared with the PKG's bradykinesia and dyskinesia scores. The PKG's daytime sleepiness score was also used.

Results

There was good correlation between Chorea Scores and the PKG's dyskinesia score (Pearson's ρ = 0.66). Correlation between the Bradykinesia Scores and the PKG's bradykinesia score was also good (Pearson's ρ = 0.51) in cases whose PKG scores were in the normal or bradykinetic range. The PKG's bradykinesia score of 23, which is in the higher range of control subjects, separated participants into those with Independence Score ≥ 80 or < 80 and a Functional Assessment (FAS) score ≥ 18 or < 18. The PKG's daytime sleep score was high in 44 % of participants, whose average time asleep was 21 % compared to 1.6 % in participants with a normal sleep index. Participants with high sleep scores were significantly more likely to have low Independence and TFC scores.

Conclusions

Measures of bradykinesia and dyskinesia from clinical scales have acceptable correlations with those from the PKG. Continuous monitoring provides information about daytime sleep, which was associated with lower functional status. Further studies and larger sample sizes are required to confirm these findings and the utility of this measure in Huntington's Disease.