Robotic algorithms that augment movement errors have been proposed as promising training strategies to enhance motor learning and neurorehabilitation. However, most research effort has focused on rehabilitation of upper limbs, probably because large movement errors are especially dangerous during gait training, as they might result in stumbling and falling. Furthermore, systematic large movement errors might limit the participants’ motivation during training. In this study, we investigated the effect of training with novel error modulating strategies, which guarantee a safe training environment, on motivation and learning of a modified asymmetric gait pattern. Thirty healthy young participants walked in the exoskeletal robotic system Lokomat while performing a foot target-tracking task, which required an increased hip and knee flexion in the dominant leg. Learning the asymmetric gait pattern with three different strategies was evaluated: (i) No disturbance: no robot disturbance/guidance was applied, (ii) haptic error amplification: unsafe and discouraging large errors were limited with haptic guidance, while haptic error amplification enhanced awareness of small errors relevant for learning, and (iii) visual error amplification: visually observed errors were amplified in a virtual reality environment. We also evaluated whether increasing the movement variability during training by adding randomly varying haptic disturbances on top of the other training strategies further enhances learning. We analyzed participants’ motor performance and self-reported intrinsic motivation before, during and after training. We found that training with the novel haptic error amplification strategy did not hamper motor adaptation and enhanced transfer of the practiced asymmetric gait pattern to free walking. Training with visual error amplification, on the other hand, increased errors during training and hampered motor learning. Participants who trained with visual error amplification also reported a reduced perceived competence. Adding haptic disturbance increased the movement variability during training, but did not have a significant effect on motor adaptation, probably because training with haptic disturbance on top of visual and haptic error amplification decreased the participants’ feelings of competence. The proposed novel haptic error modulating controller that amplifies small task-relevant errors while limiting large errors outperformed visual error augmentation and might provide a promising framework to improve robotic gait training outcomes in neurological patients.

Camera Sensor Networks (CSN) are becoming increasingly popular in a variety of security and safety-critical applications including public space surveillance, monitoring of attack-sensitive facilities, and critical infrastructure protection. Cameras in such networks are equipped with high-resolution visual sensors and on-board processors, while featuring wireless communication capabilities. These features enable the execution of various tasks, such as area coverage, activity recognition and target tracking, in a cooperative fashion. However, the performance of CSN may be compromised when faults occur, either due to unintentional software and hardware faults or as the result of a malicious attack. Paving the way for fault tolerance in CSN-based target tracking, we introduce a flexible fault model that can be used to generate different types of erroneous behaviour, thus simulating realistic faults in CSN. We also propose a fault-tolerant decentralized solution for tracking a target that passes through the area monitored by the CSN. Our simulation results indicate that the proposed solution is able to track the target reliably despite the presence of faults.

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Abstract-Emerging Camera Sensor Networks (CSN) leverage the collaboration, processing and communication capabilities of modern cameras to handle a wide variety of security and safety-critical tasks, including target tracking. However, the performance of CSN in terms of tracking accuracy can be severely degraded when faults occur. Faults may be caused by unpredictable software errors (e.g., in the image processing, feature extraction and data association modules), hardware malfunctions (e.g., in the camera mechanical parts or lens), or as the result of a malicious attack. We propose a decentralized CSN-based system for tracking multiple targets that mitigates the effect of faulty cameras. According to our findings, the proposed solution can well handle faulty camera observations and is able to reliably track a number of targets that may change over time.