Quantification of Arm Swing during Walking in Healthy Adults and Parkinson’s Disease Patients: Wearable Sensor-Based Algorithm Development and Validation

Abstract

Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson’s disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from −0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson’s disease.

The Power of Musification: Sensor‐Based Music Feedback Improves Arm Swing in Parkinson's Disease

“…The algorithm by Warmerdam and colleagues 27 was used to compute the AS parameters ROM, peak angular velocity (PAV) and regularity, which represents the similarity of neighboring swings based on the angular velocity. AS ROM values were used to calculate the non‐directional asymmetry indices (ndASI) with the formula, where R and L represent right and left AS ROM mean values 28 .…”

Digital Phenotyping in Clinical Neurology

“…158 Arm swing amplitude and peak angular velocity derived from bilaterally placed wrist IMUs were reduced in individuals with Parkinson disease compared with controls. 159 A few studies have obtained gait information using data from sensors in smartphones. In active duty Navy personnel exposed to repetitive low-level blasts associated with neurocognitive decline, there was slowed walking pace and increased stride time variability during a stepping-in-place task, as measured by accelerometers in a smartphone strapped to the thigh.…”

“…158 Arm swing amplitude and peak angular velocity derived from bilaterally placed wrist IMUs were reduced in individuals with Parkinson disease compared with controls. 159…”

A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes

“…The validity of smartphone-based sensor technologies in clinical trial setting and the smartphone-derived severity score for PD can provide an adequate measure of motor symptoms, which was shown though a phase 1 PD clinical trail including 44 PD participants and 35 healthy controls [61], as well as using smartphone sensor data from PD individuals and a novel machine learning approach [62]. Moreover, the Parkinson@Home validation study provided a new reference dataset for the development of digital biomarkers to monitor persons with PD in daily life [63], whereas wearable inertial systems such as a wearable sensor-based arm swing algorithm for patients with PD has been developed and validated presenting high accuracy and showing great potential to be used in a daily-living environment [64].…”