Rules-Based Real-Time Gait Event Detection Algorithm for Lower-Limb Prosthesis Users during Level-Ground and Ramp Walking

Abstract

Real-time gait event detection (GED) using inertial sensors is important for applications such as remote gait assessments, intelligent assistive devices including microprocessor-based prostheses or exoskeletons, and gait training systems. GED algorithms using acceleration and/or angular velocity signals achieve reasonable performance; however, most are not suited for real-time applications involving clinical populations walking in free-living environments. The aim of this study was to develop and evaluate a real-time rules-based GED algorithm with low latency and high accuracy and sensitivity across different walking states and participant groups. The algorithm was evaluated using gait data collected from seven able-bodied (AB) and seven lower-limb prosthesis user (LLPU) participants for three walking states (level-ground walking (LGW), ramp ascent (RA), ramp descent (RD)). The performance (sensitivity and temporal error) was compared to a validated motion capture system. The overall sensitivity was 98.87% for AB and 97.05% and 93.51% for LLPU intact and prosthetic sides, respectively, across all walking states (LGW, RA, RD). The overall temporal error (in milliseconds) for both FS and FO was 10 (0, 20) for AB and 10 (0, 25) and 10 (0, 20) for the LLPU intact and prosthetic sides, respectively, across all walking states. Finally, the overall error (as a percentage of gait cycle) was 0.96 (0, 1.92) for AB and 0.83 (0, 2.08) and 0.83 (0, 1.66) for the LLPU intact and prosthetic sides, respectively, across all walking states. Compared to other studies and algorithms, the herein-developed algorithm concurrently achieves high sensitivity and low temporal error with near real-time detection of gait in both typical and clinical populations walking over a variety of terrains.

Keywords: gait analysis; gait event detection; ramp walking; real-time analysis; signal processing; wearable technology.

Figure 1

LLPU participant equipped with the Xsens MVN suit: (a) frontal view; (b) sagittal view. Sensors were attached to the feet (middle of the bridge of foot), lower leg (flat on the shin bone, medial surface of the tibia, approximately 5 cm below the patella), upper leg (middle of the lateral thigh, above the knee), and pelvis (flat on the sacrum) [32]. Solid and dashed outlines represent visible and not visible sensors, respectively. Lower-leg sensors (highlighted by filled light blue boxes) were used for validating the proposed GED algorithm.

Figure 2

Experimental protocol overview for the data collection session. LGW: level-ground walking, RA: ramp ascent, RD: ramp descent.

Figure 3

Sample data displaying GED events detected (FS and FO) for LGW, RA, and RD of both intact and prosthetic sides of LLPU_P02 (transfemoral). Plot of raw angular velocity signal (light blue). FS (blue) and FO (red), Xsens (solid) and proposed algorithm (dashed).