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Treadmills provide a safe and efficient method for gait rehabilitation but treadmill based training paradigms have not been shown to create superior results when compared with traditional physical therapy methods such as overground training. One explanation for this may be that walking at a constant, fixed speed requires little mental engagement from the user, which has been postulated as a key factor in the success of motor learning. To increase mental engagement, we developed a user-driven treadmill control scheme. In this paper we use electroencephalography (EEG) to compare cortical activity during user-driven (active) walking with activity on a normal (passive) treadmill in nine healthy subjects. We used independent component analysis (ICA) to isolate brain activity from artifactual components. We fit equivalent dipole sources to each brain component and clustered these across subjects. Our analysis revealed that relative to the passive treadmill, active walking resulted in statistically significant decreases in spectral power, i.e. desynchronization, in the anterior cingulate, sensorimotor cortices, and posterior parietal lobe of the cortex. These results indicate that user-driven treadmills more fully engage the motor cortex and therefore could facilitate better training outcomes than a traditional treadmill.

A self-paced treadmill that can simulate overground walking has the potential to improve the effectiveness of treadmill training for gait rehabilitation. We have implemented a self-paced treadmill without the need for expensive equipment such as a motion capture system and an instrumented treadmill. For this, an inexpensive depth sensor, ASUS XtionTM, substitutes for the motion capture system, and a low-cost commercial treadmill is considered as the platform of the self-paced treadmill. The proposed self-paced treadmill is also convenient because the depth sensor does not require markers placed on user's body. Through pilot tests with two healthy subjects, it is quantitatively and qualitatively verified that the proposed self-paced treadmill achieves similar performance as one which utilizes a commercial motion capture system (VICON) as well as an instrumented treadmill.

Treadmill-based locomotor training should simulate overground walking as closely as possible for optimal skill transfer. The constant speed of a standard treadmill encourages automaticity rather than engagement and fails to simulate the variable speeds encountered during real-world walking. To address this limitation, this paper proposes a user-driven treadmill velocity control scheme that allows the user to experience natural fluctuations in walking velocity with minimal unwanted inertial force due to acceleration/deceleration of the treadmill belt. A smart estimation limiter in the scheme effectively attenuates the inertial force during velocity changes. The proposed scheme requires measurement of pelvic and swing foot motions, and is developed for a treadmill of typical belt length (1.5 m). The proposed scheme is quantitatively evaluated here with four healthy subjects by comparing it with the most advanced control scheme identified in the literature.