Inertial sensor-based knee flexion/extension angle estimation

Abstract

A new method for estimating knee joint flexion/extension angles from segment acceleration and angular velocity data is described. The approach uses a combination of Kalman filters and biomechanical constraints based on anatomical knowledge. In contrast to many recently published methods, the proposed approach does not make use of the earth's magnetic field and hence is insensitive to the complex field distortions commonly found in modern buildings. The method was validated experimentally by calculating knee angle from measurements taken from two IMUs placed on adjacent body segments. In contrast to many previous studies which have validated their approach during relatively slow activities or over short durations, the performance of the algorithm was evaluated during both walking and running over 5 minute periods. Seven healthy subjects were tested at various speeds from 1 to 5 miles/hour. Errors were estimated by comparing the results against data obtained simultaneously from a 10 camera motion tracking system (Qualysis). The average measurement error ranged from 0.7 degrees for slow walking (1 mph) to 3.4 degrees for running (5mph). The joint constraint used in the IMU analysis was derived from the Qualysis data.Limitations of the method, its clinical application and its possible extension are discussed.

Kalman-Filtering-Based Joint Angle Measurement with Wireless Wearable Sensor System for Simplified Gait Analysis

“…This simple sensor attachment method is important for practical clinical applications. Generally, in order to realize high measurement accuracy, exact positioning of sensors or measurement of sensor positions are required [2], [8]. It is considered that these initial settings or calibration process severely affect practical use of the sensor system.…”

“…Measurement of inclination angles of body segments or joint angles has been studied using inertial sensors [1]- [8]. However, a significant problem of measurement with gyroscopes is error accumulation in the integral of sensor output, which is caused by offset drift of gyroscope.…”

“…We showed effectiveness of the Kalman filtering based joint angle measurement on the knee and ankle joints [5], [9]. The Kalman filter was also effective in correcting measured shank inclination angle [4] and knee joint angle [8]. However, joint angle measurement using the Kalman filter has not been evaluated sufficiently considering clinical applications.…”

Method analysis of accelerometers and gyroscopes in running gait: A systematic review

“…Of the 38 articles included in this review 23 articles utilised accelerometers and/or gyroscopes during running gait to identify coach orientated kinematic parameters (See Table 1.2). 48 Bergamini et al 17 Bichler et al 18 Cooper et al 19 Hausswirth et al 11 Heiden et al 49 Le Bris et al 31 Lee et al 32 Lee et al 47 McCurdy et al 50 McGrath et al 13 Mercer et al 35 Mercer et al 36 Neville et al 51 Neville et al 52 O'Donovan et al 20 Purcell et al 53 Stohrmann et al 22 Stohrmann et al 21 Stohrmann et al 23 Tan et al 14 Wixted et al 54 Yang et al 15 Step/Stride Frequency…”

Research on Non-Invasive Wireless Wearable Sensor System Using Virtual and Physical Sensors for Lower Limb Motion Analysis

“…However, since the system is space-consuming and expensive, it is not applicable for the out-lab ambulatory estimation of lower limb posture in ordinary life. Recently, many ambulatory systems for various clinical applications have been developed to monitor physical activities, for example, to estimate body segment orientation by integrating angular velocity of the body segments measured by gyroscopes [3], or estimating knee joint FE angle using segmental acceleration and angular velocity data using inertial sensor-based method [4].…”