Wearables for Movement Analysis in Healthcare

Capodaglio, Paolo; Cimolin, Verônica

doi:10.3390/s22103720

Cited by 1 publication

(1 citation statement)

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“…They are inexpensive and allow accurate assessment of human movement in the laboratory, at home, and outside of the house [20]. In recent decades, wearable inertial sensors have been used for a large number of medical applications [21], including FoG monitoring. In this context, many solutions have been proposed based on one or more sensors positioned on different parts of the body to record motion data and automatically detect FoG [22].…”

Section: Technologies For the Automatic Recognition Of Fogmentioning

confidence: 99%

Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson’s Disease

Borzì

Sigcha

Olmo

2023

Sensors

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Freezing of gait (FoG) is a disabling clinical phenomenon of Parkinson’s disease (PD) characterized by the inability to move the feet forward despite the intention to walk. It is one of the most troublesome symptoms of PD, leading to an increased risk of falls and reduced quality of life. The combination of wearable inertial sensors and machine learning (ML) algorithms represents a feasible solution to monitor FoG in real-world scenarios. However, traditional FoG detection algorithms process all data indiscriminately without considering the context of the activity during which FoG occurs. This study aimed to develop a lightweight, context-aware algorithm that can activate FoG detection systems only under certain circumstances, thus reducing the computational burden. Several approaches were implemented, including ML and deep learning (DL) gait recognition methods, as well as a single-threshold method based on acceleration magnitude. To train and evaluate the context algorithms, data from a single inertial sensor were extracted using three different datasets encompassing a total of eighty-one PD patients. Sensitivity and specificity for gait recognition ranged from 0.95 to 0.96 and 0.80 to 0.93, respectively, with the one-dimensional convolutional neural network providing the best results. The threshold approach performed better than ML- and DL-based methods when evaluating the effect of context awareness on FoG detection performance. Overall, context algorithms allow for discarding more than 55% of non-FoG data and less than 4% of FoG episodes. The results indicate that a context classifier can reduce the computational burden of FoG detection algorithms without significantly affecting the FoG detection rate. Thus, implementation of context awareness can present an energy-efficient solution for long-term FoG monitoring in ambulatory and free-living settings.

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Section: Technologies For the Automatic Recognition Of Fogmentioning

confidence: 99%