Abstract
In this paper, we present a new method for estimating knee joint angle using force myography. The technique utilized force myogram signals from thigh muscles while subjects walked on a treadmill at different speeds, i.e., slow, medium, fast, and run. An eight-channel in-house force myography (FMG) data acquisition system was developed to collect the data wirelessly from seven healthy subjects and a transfemoral amputee. An artificial neural network was employed to estimate the knee joint angle from force myogram signals. The root-mean-square error across the healthy subjects was 6.9±1.5° at slow (1.5km/hr), 6.5±1.3° at medium (4km/hr), 7.4±2.2° at fast (6km/hr) speeds, and 8.1±2.2° while running (8km/hr). The root-mean-square error, across the trials, for the transfemoral amputee was 4.0±1.2° at slow (1km/hr), 3.2±0.6° at medium (2km/hr) and 3.8±0.9° at fast (3km/hr) speeds. The proposed approach is useful in real-time gait analysis. The system is easily wearable, convenient in out-door use, portable, and commercially viable.
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