Detection of Hemiplegic Walking Using a Wearable Inertia Sensing Device

Abstract

Hemiplegia is a symptom that is caused by reduced sensory and motor ability on one side of the body due to stroke-related neural defects. Muscular weakness and abnormal sensation that is induced by hemiplegia usually lead to motor impairments, such as difficulty in controlling the trunk, unstable balance, and poor walking ability. Therefore, most hemiplegia patients show defective and asymmetric gait pattern. The purpose of this study is to distinguish hemiplegic gait by extracting simple characteristics of acceleration signals that are caused by asymmetry during walking using a wearable system. The devised wearable system was equipped with a three-axis accelerometer and a three-axis gyroscope. We selected 165 candidate features without step detection. A random forest algorithm was used for the classification, and the forward search algorithm was also used for optimal feature selection. The developed system and algorithms were verified clinically in 15 normal subjects and 20 hemiplegia patients that were undergoing stroke treatment, and 26 subject’s data was used for training, including validation, and nine subject’s data used for test. As a result of test set, the accuracy, sensitivity, specificity and positive predictive value were 100.0%, with the two classification attributes of standard deviation of points perpendicular to the axis of line of identity of Poincaré plot of angular velocity around vertical axis and kurtosis of frequency of angular velocity around longitudinal axis.

Using an Accelerometer-Based Step Counter in Post-Stroke Patients: Validation of a Low-Cost Tool

“…Moreover, the testing of patients was not filmed in order to perform a visual analysis and differentiate between gait patterns. The performance of a visual analysis in future studies regarding stroke patients and step counters could be important both to improve the accuracy of human manual counting and to better understand how the quality of gait influences the accuracy of step detection [26]. Because of the commercial nature of the device considered, we had no control over the algorithm of the interpretation of raw data.…”

“…Step counters can be used to detect post-stroke patients' activity levels [25][26][27], assess gait and balance parameters [28,29], and guide the patients in performing exercises [30], as well as acting as a stimulus to increase in-and out-patient activity levels [14]. The literature shows that the monitoring of patients' mobility in an inpatient setting can give information similar to advanced and time-consuming techniques, such as behavioral mapping, and it can be useful to collect the activity levels of hospitalized patients [31].…”

Walking behaviors for stroke survivors: comparison between straight line and curved path

“…and integrated walking while adapting to the changes of environmental conditions. Walking performance for stroke survivors is a prerequisite for performing a safe recovery of activities and participation restrictions and therefore, it is necessary to understand the gait characteristics in various environmental demands within the rehabilitation setting [1,[4][5][6].…”

Deep Convolutional Neural Network-Based Hemiplegic Gait Detection Using an Inertial Sensor Located Freely in a Pocket

“…In addition, the most recent personal smart and wearable devices are equipped with an inertial sensor, so it is easy to perform gait analysis without an additional system. Inertial sensors are widely applied for gait analysis for purposes that range from detecting gait-specific features [1,2] and general gait events [3][4][5][6] to clinical applications in the rehabilitation training monitoring of hemiplegic patients [7][8][9][10][11] and patients with Parkinson's disease [12,13] or Alzheimer's disease [14].…”