Wearable-Sensor-Based Detection and Prediction of Freezing of Gait in Parkinson’s Disease: A Review

Abstract

Freezing of gait (FOG) is a serious gait disturbance, common in mid- and late-stage Parkinson’s disease, that affects mobility and increases fall risk. Wearable sensors have been used to detect and predict FOG with the ultimate aim of preventing freezes or reducing their effect using gait monitoring and assistive devices. This review presents and assesses the state of the art of FOG detection and prediction using wearable sensors, with the intention of providing guidance on current knowledge, and identifying knowledge gaps that need to be filled and challenges to be considered in future studies. This review searched the Scopus, PubMed, and Web of Science databases to identify studies that used wearable sensors to detect or predict FOG episodes in Parkinson’s disease. Following screening, 74 publications were included, comprising 68 publications detecting FOG, seven predicting FOG, and one in both categories. Details were extracted regarding participants, walking task, sensor type and body location, detection or prediction approach, feature extraction and selection, classification method, and detection and prediction performance. The results showed that increasingly complex machine-learning algorithms combined with diverse feature sets improved FOG detection. The lack of large FOG datasets and highly person-specific FOG manifestation were common challenges. Transfer learning and semi-supervised learning were promising for FOG detection and prediction since they provided person-specific tuning while preserving model generalization.

Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test

“…Recent studies underline the importance of using relatively large datasets for studying FOG detection and specifically for ML approaches [6,9,13]. For example, a 2019 review screened 68 papers that aimed to detect FOG [13], and the largest studied population was only 32 FOG-PD subjects. In the present study, records from 71 subjects with a total of 6.75 hours of FOG episodes were included.…”

“…The objective assessment of FOG, theoretically, can be achieved with wearable inertial sensors. Inertial sensors can be placed at various body locations to obtain a complete picture of the patient’s movement [ 13 , 14 ]. Analyzing FOG episodes with inertial sensors enables objective measurement of FOG duration, its dynamics, the number of episodes, and the context in which they occurred [ 8 , 12 , 14 , 15 ].…”

Deep Phenotyping of Parkinson’s Disease

“…For the past decade, accelerometers have helped quantify motor features of PD [17][18][19][20][21][22]. However, most of these studies (Table 1) have limited their assessments to tasks performed in the clinic [18].…”

Die Geschichte des ‚Freezing-of-gait‘ beim Parkinson-Syndrom – vom Phänomen zum Symptom

“…Dabei wurde festgestellt, dass die Sensoren und die damit verbundenen Algorithmen zwar sehr gut in der Lage waren, in standardisierten und vorgegebenen Gangsequenzen die Freezing-Episoden zu identifizieren, jedoch der Transfer in die nichtstandardisierte und nichtsupervidierte Ganganalyse mittels Sensoren im häuslichen Umfeld bisher kaum untersucht ist [28]. Speziell die Prädiktion und Detektion von FOG wird durch optimierte Algorithmik immer besser [29], wobei die Verwendung von Sensor-basierter Ganganalyse als objektiver Outcome in klinischen Pharma-Studien noch aussteht. Während die Sensor-basierte Bewegungsanalyse einen wahrhaften Boom erfährt, gibt es jedoch 2019 weiterhin nur wenige, kleine Studien, die versuchen, FOG in häuslicher Umgebung zu detektieren [23].…”