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In this research, a marker-less 'smart hallway' is proposed where stride parameters are computed as a person walks through an institutional hallway. Stride analysis is a viable tool for identifying mobility changes, classifying abnormal gait, estimating fall risk, monitoring progression of rehabilitation programs, and indicating progression of nervous system related disorders. Methods: Smart hallway was build using multiple Intel RealSense D415 depth cameras. A novel algorithm was developed to track a human foot using combined point cloud data obtained from the smart hallway. A method was implemented to separate the left and right leg point cloud data, then find the average foot dimensions. Foot tracking was achieved by fitting a box with average foot dimensions to the foot, with the box's base on the foot's bottom plane. A smart hallway with this novel foot tracking algorithm was tested with 22 able-bodied volunteers by comparing marker-less system stride parameters with Vicon motion analysis output. Results: With smart hallway frame rate at approximately 60fps, temporal stride parameter absolute mean differences were less than 30ms. Random noise around the foot's point cloud was observed, especially during foot strike phases. This caused errors in medial-lateral axis dependent parameters such as step width and foot angle. Anterior-posterior dependent (stride length, step length) absolute mean differences were less than 25mm. Conclusion: This novel marker-less smart hallway approach delivered promising results for stride analysis with small errors for temporal stride parameters, anterior-posterior stride parameters, and reasonable errors for medial-lateral spatial parameters.

Estimation of channel is a significant issue in wireless communication. In this paper, TS fuzzy Kalman Filter based channel impulse response(CIR) estimation, for the time varying velocity of the receiver in a Multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) system is being proposed. The channel is being modeled using second order auto regressive (AR) random model. Linearization of channel estimation is done using fuzzy logic and Kalman filter is used to estimate the channel. For fast time varying channel, fuzzy based channel impulse response estimation is a low complex technique when compared to conventional filters.

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