Online Decoding of Hidden Markov Models for Gait Event Detection Using Foot-Mounted Gyroscopes

• Published in: IEEE journal of biomedical and health informatics Vol. 18; no. 4; pp. 1122 - 1130
• Main Authors: Mannini, Andrea, Genovese, Vincenzo, Sabatin, Angelo Maria
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Decoding; Foot; Foot - physiology; Gait - physiology; Gait event detection; gyroscope; hidden Markov model (HMM); Hidden Markov models; human movement analysis; Humans; Internet; Legged locomotion; Markov Chains; Monitoring, Ambulatory - instrumentation; Monitoring, Ambulatory - methods; Sensors; short-time Viterbi (STV); Signal Processing, Computer-Assisted; Telemedicine; Vectors; Viterbi algorithm
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2013.2293887

In this paper, we present an approach to the online implementation of a gait event detector based on machine learning algorithms. Gait events were detected using a uniaxial gyro that measured the foot instep angular velocity in the sagittal plane to feed a four-state left-right hidden Markov model (HMM). The short-time Viterbi algorithm was used to overcome the limitation of the standard Viterbi algorithm, which does not allow the online decoding of hidden state sequences. Supervised learning of the HMM structure and validation with the leave-one-subject-out validation method were performed using treadmill gait reference data from an optical motion capture system. The four gait events were foot strike, flat foot (FF), heel off (HO), and toe off. The accuracy ranged, on average, from 45 ms (early detection, FF) to 35 ms (late detection, HO); the latency of detection was less than 100 ms for all gait events but the HO, where the probability that it was greater than 100 ms was 25%. Overground walking tests of the HMM-based gait event detector were also successfully performed.