Evaluating Gait Impairment in Parkinson’s Disease from Instrumented Insole and IMU Sensor Data

Abstract

Parkinson’s disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients’ mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment.

Assisted technology in Parkinson's disease gait: what's up?

“…20 Other studies monitoring gait parameters in PD used wearable sensors or wearable devices based on different types of sensors. [31][32][33][34][35][36][37][38][39] Figure 1 Flow diagram of literature search strategy.…”

“…These insoles are composed of an accelerometer, gyroscope, and sometimes a magnetometer attached to the patient and can measure the linear and angular velocity, acceleration, and other gait parameters. 35 Sensors may also be placed under smart shoes and inertial microelectromechanical system sensors attached to the patients to measure stride length, gait velocity, range of motion of the ankle, knee, and hip joints of individuals with PD. 36 Other studies are focusing on developing new SWD to detect the occurrence of freezing of gait (FOG) and its discrimination in PD (freezing, shuffling, and trembling), 37 and promising resources may be launched in the near future.…”

A Computer Vision-Based System to Help Health Professionals to Apply Tests for Fall Risk Assessment

“…Therefore, there exists a need to automate diagnosis and treatments (even in the context of rehabilitation and fall risk monitoring, and this is what some investigations propose. The vast majority of them have chosen to integrate IMUs (Inertial Measurement Units), to obtain signals and associate them with the score of one of the previously described tests [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. This solution has some drawbacks and limitations, since it is required that people wear the equipment on different parts of the body.…”

Detecting Minor Symptoms of Parkinson’s Disease in the Wild Using Bi-LSTM with Attention Mechanism

“…In recent years, there has been growing interest in utilizing artificial intelligence (AI) techniques to develop noninvasive and accessible methods for early PD detection [ 21 ]. The advent of wearable devices, such as accelerometers embedded in smartphones or smartwatches, has provided a wealth of data which can be leveraged to extract meaningful information about an individual’s movement patterns and identify potential biomarkers for PD [ 22 , 23 ]. The use of machine learning (ML) and deep learning (DL) techniques that employ sensor signals (e.g., accelerometers, gyroscopes, pressure sensors) has garnered increased attention [ 24 , 25 , 26 ].…”