Chemotherapy-induced fatigue estimation using hidden Markov model
RIS ID
132074
Abstract
Chemotherapy-induced fatigue undermines the physical performance and alter gait behaviour of patients. In clinics, there is not a well-established method to objectively assess the effects of chemotherapy-induced fatigue on gait characteristics. Clinical trials commonly use 6 Minute Walking Tests (6MWT) to assess patients' gait. However, these studies only measure the distance that patients can walk. The distance does not provide comprehensive information about variations in ambulatory motion characteristics and body postural behaviour which can more appropriately describe the fatigue effects on general physical performance. Gait characteristics provide a manifestation of relationships between muscular and cardiovascular fitness status and physical motions. Hence, an assessment of gait characteristics provides more appropriate information about the effects of chemotherapy-induced fatigue on gait behaviour. A novel approach is proposed to objectively assess the impacts of chemotherapy-induced fatigue on cancer gait by analysing the gait characteristics during 6MWT. The joint angles of the lower body segments are measured by inertial sensors and modelled through a Hidden Markov Model (HMM) with Gaussian emissions. A Gaussian clustering method classifies the joint angles of first gait cycle to determine the six gait phases of a normal gait as initial training values. A comparison of gait characteristics before and after chemotherapy-induced fatigue determines the gait abnormalities. The method is applied to four cancer patients and outcomes are benchmarked against the gait of a healthy subject before and after running program-induced fatigue. The results indicate a more accurate quantitative-based tool to measure the effects of chemotherapy-induce fatigue on gait and physical performance.
Publication Details
S. Ameli, F. Naghdy, D. Stirling, G. Naghdy & M. Aghmesheh, "Chemotherapy-induced fatigue estimation using hidden Markov model," Biocybernetics and Biomedical Engineering, vol. 39, (1) pp. 176-187, 2019.