The gold standards for gait analysis are instrumented walkways and marker-based motion capture systems, which require costly infrastructure and are only available in hospitals and specialized gait clinics. Even though the completeness and the accuracy of these systems are unquestionable, a mobile and pervasive gait analysis alternative suitable for non-hospital settings is a clinical necessity. Using inertial sensors for gait analysis has been well explored in the literature with promising results. However, the majority of the existing work does not consider realistic conditions where data collection and sensor placement imperfections are imminent. Moreover, some of the underlying assumptions of the existing work are not compatible with pathological gait, decreasing the accuracy. To overcome these challenges, we propose a foot-mounted inertial sensor-based gait analysis system that extends the well-established zero-velocity update and Kalman filtering methodology. Our system copes with various cases of data collection difficulties and relaxes some of the assumptions invalid for pathological gait (e.g., the assumption of observing a heel strike during a gait cycle). The system is able to extract a rich set of standard gait metrics, including stride length, cadence, cycle time, stance time, swing time, stance ratio, speed, maximum/minimum clearance and turning rate. We validated the spatio-temporal accuracy of the proposed system by comparing the stride length and swing time output with an IR depth-camera-based reference system on a dataset comprised of 22 subjects. Furthermore, to highlight the clinical applicability of the system, we present a clinical discussion of the extracted metrics on a disjoint dataset of 17 subjects with various neurological conditions.

Inertial Sensor-Based Robust Gait Analysis in Non-Hospital Settings for Neurological Disorders

Multi-sensor fusion for body sensor network in medical human–robot interaction scenario

Gait and posture are regular activities which are fully controlled by the sensorimotor cortex. In this study, fluctuations of joint angle and asymmetry of foot elevation in human walking stride records are analyzed to assess gait in healthy adults and patients affected with gait disorders. This paper aims to build a low-cost, intelligent and lightweight wearable gait analysis platform based on the emerging body sensor networks, which can be used for rehabilitation assessment of patients with gait impairments. A calibration method for accelerometer and magnetometer was proposed to deal with ubiquitous orthoronal error and magnetic disturbance. Proportional integral controller based complementary filter and error correction of gait parameters have been defined with a multi-sensor data fusion algorithm. The purpose of the current work is to investigate the effectiveness of obtained gait data in differentiating healthy subjects and patients with gait impairments. Preliminary clinical gait experiments results showed that the proposed system can be effective in auxiliary diagnosis and rehabilitation plan formulation compared to existing methods, which indicated that the proposed method has great potential as an auxiliary for medical rehabilitation assessment.

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MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion

Gait analysis has become an important tool for diagnosing disease and evaluating disease progression. Currently gait analysis was mainly conducted by experienced physicians and relies on medical observation or complex medical equipment; hence, the application has been limited. This research aims to build an ambulatory gait analysis system based on the emerging body sensor networks. A calibration method for a magnetometer was proposed to deal with ubiquitous a magnetic disturbance. A proportional integral controller-based complementary filter and error correction of gait parameters have been defined with a multi-level data fusion algorithm. Preliminary gait analysis trials were conducted on both healthy subjects and patients with abnormal gait. Experimental results indicated that the proposed scheme has significant advantages in terms of test accuracy and efficiency, as well as operation complexity compared with conventional gait analysis approaches. Accordingly, measurement of gait abnormality may provide a wealth of information regarding neuromotor status and the characterization of some neurological disorders. It is concluded that the proposed gait analysis system has great potential as an auxiliary for medical rehabilitation.

Body Sensor Network-Based Robust Gait Analysis: Toward Clinical and at Home Use

Parkinson's disease monitoring from gait analysis via foot-worn sensors

This paper presents a MARG Sensors based walking motion analysis method that can be used for rehabilitation treatment evaluation under ambulatory conditions in daily lives. A commercial inertial measure unit MTx is fixed on human shank segment. Then digital data box worn on the waist transmits the raw data to laptop with Blue-tooth for post processing. Subjects are required to perform level walking and upstairs walking respectively. After the initial alignment and quaternion convergence, the proposed method fuses the inertial data using Kalman filter based on quaternion. Walking trajectory and knee joint angle are estimated and validated by available ground truth. Experiment results demonstrate that the proposed method has a good performance at both motion patterns. It also shows that no significant drifts exist in the overall results presented in the paper. The method can also be adapted to measure other sites of articulation such as ankle and elbow.

Ambulatory estimation of 3D walking trajectory and knee joint angle using MARG Sensors

Gait analysis by using wearable inertial sensors has gained tremendous achievements in recent years. A limitation of using inertial measurement unit (IMU) is that inertial signals heavily rely on the correctness of sensor installation. A slight installation orientation error could lead to inaccurate results for quantitative estimation of gait parameters. In this paper, a calibration method was proposed to deal with installation orientation errors of IMU, where the errors were considered as an IMU frame rotating around the standard coordinate system. The proposed method could generate a three-dimensional rotation matrix to calibrate gait signals. By using the calibrated signals, estimation of gait parameters such as calculation of stride length and detection of gait phase can be more accurate. An experiment was performed on our gait monitoring platform based on IMU, and the experimental results demonstrate the effectiveness of the proposed method.

A method to calibrate installation orientation errors of inertial sensors for gait analysis

This paper presents a new system for human gait data acquisition and analysis. The system is based on a network of wearable nodes with embedded sensors, which consists of two data acquisition nodes and a handled control center node. And the system is not restricted to a specific environment in the experiment. In this paper, we discuss the initial design of the hardware for data acquisition and the software for data analysis. Then ten subjects took participate in the actual environment test and each subject tested for five times. The experimental results demonstrate that the system has a good performance to acquire gait parameters, such as gait cycle, stride length, and gait phases, which can provide important clues for the diagnosis of the disease.

A System for Human Gait Analysis Based on Body Sensor Network

This paper designs a UAV (Unmanned Aerial Vehicles) wireless data acquisition system, which consists of a UAV surface signal acquisition subsystem and a ground data processing subsystem. The system can continuously measure air pressure, acceleration, temperature and other signals of the UAV surface in real time. These signals are sent to the ground base station by wireless or cable transmission for further process. Then both the original signals data and the processed signals data will be stored at the ground base station. This system mainly adopts STM32F103 as microcontroller and XBee-PRO 900HP as wireless transmission module. Experiments show that the system meets the requirements of accurate, real-time, continuously and long-time monitoring of UAV surface signals.

Jijian Hou

Papers

Ambulatory estimation of 3D walking trajectory and knee joint angle using MARG Sensors

A method to calibrate installation orientation errors of inertial sensors for gait analysis

A System for Human Gait Analysis Based on Body Sensor Network

A wireless data acquisition system for detecting the flight status of Unmanned Aerial Vehicles