Unconstrained detection of freezing of Gait in Parkinson's disease patients using smartphone

A critical symptom of Parkinson’s disease (PD) is the occurrence of Freezing of Gait (FOG), an episodic disorder that causes frequent falls and consequential injuries in PD patients. There are various auditory, visual, tactile, and other types of stimulation interventions that can be used to induce PD patients to escape FOG episodes. In this article, we describe a low cost wearable system for non-invasive gait monitoring and external delivery of superficial vibratory stimulation to the lower extremities triggered by FOG episodes. The intended purpose is to reduce the duration of the FOG episode, thus allowing prompt resumption of gait to prevent major injuries. The system, based on an Android mobile application, uses a tri-axial accelerometer device for gait data acquisition. Gathered data is processed via a discrete wavelet transform-based algorithm that precisely detects FOG episodes in real time. Detection activates external vibratory stimulation of the legs to reduce FOG time. The integration of detection and stimulation in one low cost device is the chief novel contribution of this work. We present analyses of sensitivity, specificity and effectiveness of the proposed system to validate its usefulness.

Section: Resultsmentioning

confidence: 99%

A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait

Punin

Barzallo

Clotet

et al. 2019

“…To further improve FOG detection performance, multiple features and machine-learning (ML) techniques have been used, such as neural networks [36,38,55,66,76,80,85,86,88,89,91], decision trees [25,39,42,45,52,54,58,85], random forests, [39,42,43] naïve Bayes [42,43], nearest neighbor [42], and support vector machines [64,71,74,75,81,83,86]. In addition, anomaly detection [20] and unsupervised machine learning have been attempted [87], but not extensively explored.…”

Section: Discussionmentioning

confidence: 99%

“…LogitBoosting (logistic boosting) [126], RUSBoosting [127], and RobustBoosting [128] are extensions of AdaBoosting that can further improve performance [85]. Decision trees for FOG detection included ensembles of trees and boosting techniques [42,43,85], with performance results ranging from 66.25% to 98.35% for sensitivity and 66.00% to 99.72% for specificity [25,39,42,43,45,52,54,58,85].…”

Section: Discussionmentioning

confidence: 99%

“…When evaluating a classification system, ideally, different data are used for training and testing, as in [25,38,51,55,56,64,66,67,85,96,97,99,100], in order to prevent model performance overestimation that can occur when the model is evaluated using data used in model training. Cross-validation is often used when the dataset size is limited, as done in [24,31,32,33,39,42,43,45,52,54,58,74,75,81,86,87,88,89,90,98]. For FOG research, leave-one-person-out cross-validation was the most common.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Pardoel

Kofman

Nantel

et al. 2019

138

111

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.

“…Much work has been done in the last decade in the field of telemedicine with respect to the home assistance of patients affected by chronic diseases and significant effort has been devoted in particular to wearable sensors for the detection of motion symptoms [20,21,22,23,24,25]. In the specific context of the gait analysis and FOG detection in PD, inertial measurement units (IMU) are mainly used [24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47]. In very few cases, different signals are considered.…”

Section: Introductionmentioning

confidence: 99%

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

Mazzetta

Zampogna

Suppa

et al. 2019

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.