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This paper presents a gait phase detection algorithm for providing feedback in walking with a robotic prosthesis. The algorithm utilizes the output signals of a wearable wireless sensory system incorporating sensorized shoe insoles and inertial measurement units attached to body segments. The principle of detecting transitions between gait phases is based on heuristic threshold rules, dividing a steady-state walking stride into four phases. For the evaluation of the algorithm, experiments with three amputees, walking with the robotic prosthesis and wearable sensors, were performed. Results show a high rate of successful detection for all four phases (the average success rate across all subjects >90%). A comparison of the proposed method to an off-line trained algorithm using hidden Markov models reveals a similar performance achieved without the need for learning dataset acquisition and previous model training.

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Roll dynamics in heavy freight vehicles is characterized in driving conditions by the lateral load transfer coefficient. The coefficient tracking requires sophisticated measurement systems for assessing the wheels’ normal loading. In this paper a new recursive non-linear lateral load transfer estimator employing sensors that are already used on a vehicle in combination with additional units that are not sophisticated for the implementation is described. The proposed algorithm integrates two subsystems—separate estimators for the tractor and the trailer. The first estimator is based on slip information of the driving wheels, while the second estimator fuses the tractor estimation and the modelled trailer dynamics by the extended Kalman filter. The novel approach was evaluated on a sophisticated computer simulator of the Freightliner Century Class tractor semitrailer. The simulation analysis shows that the proposed algorithm provides robust operation and good tracking performance. This approach enables a practical realization of a low-cost solution for the rollover prevention in real heavy freight vehicles.

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