Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals

Abstract

We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson’s disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art.

Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

“…Mazzeta et al [69] 2019 Propose a wearable sensor system for auto-continuous analysis of FoG in PD patients.…”

“…Unfortunately, EMG has limitations and considerations [81,83,84 EMG and SEMG have been used to identify certain gait characteristics, distinguish compensatory balance responses, and develop and improve methods used to assess balance. Continuous EMG analysis in patients with neurological disorders provide relevant diagnostic contributions in terms of nosological classification, localization of focal impairments, detection of pathophysiological mechanisms, and functional assessment to supplement the clinical evaluation of neuromuscular disorders [31,52,69,73].…”

“…Step and/or stride detection is easier when using insole pressure sensors since the pressure applied to the sensor when the foot is in contact with the ground is higher than when it is not in contact with the ground [31,46,52,54,69,73]. However, people that are affected by neurological disorders tend to shuffle and/or drag their feet when walking, performing short and dragged steps, which makes it harder to detect heel strikes or foot contact [106].…”

Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

“…For Parkinson's disease (PD), several approaches have been developed for wearable monitoring systems during the last years, with detection of bradykinesia and tremor [97][98][99][100][101]. Machine learning has been used to detect circadian rhythms and sleep, motor, and autonomic disruption, which is suitable for objective and non-invasive monitoring of PD patients [98].…”

Shedding Light on Nocturnal Movements in Parkinson’s Disease: Evidence from Wearable Technologies

“…This approach, also termed “sensor fusion”, provides more complete information on body motion, including data on muscle activity through sEMG wireless sensors [ 66 ]. Accordingly, innovative devices composed of both IMUs and sEMG are increasingly used to optimize the objective recognition of motor disorders in PD patients [ 67 , 68 ]. When using innovative wearable sensors, it is essential to consider also some limitations.…”