Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders

Abstract

The aim of this study was to conduct a comprehensive analysis of the placement of multiple wearable sensors for the purpose of analyzing and classifying the gaits of patients with neurological disorders. Seven inertial measurement unit (IMU) sensors were placed at seven locations: the lower back (L5) and both sides of the thigh, distal tibia (shank), and foot. The 20 subjects selected to participate in this study were separated into two groups: stroke patients (11) and patients with neurological disorders other than stroke (brain concussion, spinal injury, or brain hemorrhage) (9). The temporal parameters of gait were calculated using a wearable device, and various features and sensor configurations were examined to establish the ideal accuracy for classifying different groups. A comparison of the various methods and features for classifying the three groups revealed that a combination of time domain and gait temporal feature-based classification with the Multilayer Perceptron (MLP) algorithm outperformed the other methods of feature-based classification. The classification results of different sensor placements revealed that the sensor placed on the shank achieved higher accuracy than the other sensor placements (L5, foot, and thigh). The placement-based classification of the shank sensor achieved 89.13% testing accuracy with the Decision Tree (DT) classifier algorithm. The results of this study indicate that the wearable IMU device is capable of differentiating between the gait patterns of healthy patients, patients with stroke, and patients with other neurological disorders. Moreover, the most favorable results were reported for the classification that used the combination of time domain and gait temporal features as the model input and the shank location for sensor placement.

Accuracy Verification of Spatio-Temporal and Kinematic Parameters for Gait Using Inertial Measurement Unit System

“…Five sensors of LEGSys+ (5.0 cm × 4.2 cm × 1.2 cm) were connected to the computer via Bluetooth and included a 3-axis accelerometer, gyroscope, and magnetometer [21]. For the orientation of the sensor's axis, the x-axis was set to a vertical direction, the y-axis was set to an antero-posterior direction, and the z-axis was set to a medio-lateral direction [22]. A sensor was attached to the center of the posterior superior iliac spine and the anterior surfaces of both shin (3 cm above the ankle) and thigh (3 cm above the knee) ( Figure 1).…”

“…Sensors 2020, 20, x FOR PEER REVIEW 3 of 9 the y-axis was set to an antero-posterior direction, and the z-axis was set to a medio-lateral direction [22]. A sensor was attached to the center of the posterior superior iliac spine and the anterior surfaces of both shin (3 cm above the ankle) and thigh (3 cm above the knee) ( Figure 1).…”

Fast Wearable Sensor–Based Foot–Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping

“…Human motion recognition (HMR) is a technology domain that recognizes and distinguishes different types of human activities using sensor data [ 1 ]; it is widely used in rehabilitation and medical treatment like the classification and rehabilitation evaluation of patients with hip osteoarthritis, neurological disorders such as stroke, and Parkinson’s disease through gait analysis [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. It also has been used in training assistance like exercise coaching through motion tracking and feedback, speed and position tracking in sports training [ 12 , 13 , 14 , 15 , 16 , 17 ], sudden fall prevention [ 18 ] along with the development of wearable sensor technology.…”

Perspectives on the prospective development of stroke-specific lower extremity wearable monitoring technology: a qualitative focus group study with physical therapists and individuals with stroke

“…For example, consumer activity monitors are often limited by a minimum walking speed or movement amplitude in order to provide accurate and reliable feedback [15,16]. Research efforts have attempted to adapt available wearable monitoring technology to meet the needs of individuals with stroke with increasing accuracy, from simple solutions such as wearing hip-situated fitness trackers at the ankle [17,18], to developing software algorithms to analyze captured data to recognize movements patterns specific to stroke [19][20][21]. The advances in wearable monitoring have reached a point at which designing stroke-specific wearable monitoring technology is a realistic priority to assess outcome and enhance rehabilitation interventions [22].…”