There is increasing interest in shared control for assistive robotics with adaptable levels of supervised autonomy. In this work, we present a user-adaptive multi-layer shared control scheme for control of assistive devices. The system leverages the advantages of brain-inspired hyperdimensional computing (HDC) for classification & recall of reactive robotic behavior including high performance, computational efficiency and intelligent sensor fusion, to execute actuation based on the user's goal while alleviating the burden of fine control. Using a multi-modal dataset of activities of daily living, we first recognize the user's most recent behaviors, then predict the user's next action based on their habitual action sequences, and finally, determine actuation through HDC recall-based shared control which intelligently deliberates between the predicted action and sensor feedback-based autonomy. In this work, we independently implement each layer to achieve >92% accuracy and then integrate the layers and discuss the combined performance and methods to reduce accumulated error.

Hyperdimensional computing (HDC) is a brain-inspired computing paradigm that operates on pseudo-random hypervectors, an information-rich, hardware-efficient representation that is robust to noise and facilitates learning with limited training data. This work explores how robot navigation tasks can leverage the high-capacity hypervector representation to enable behavioral prioritization through a weighted encoding of heterogeneous sensor information. Experiments over 100 trials in each of the 100 randomly generated obstacle maps demonstrate that the proposed weighted sensor encoding scheme boosts the success rate of the navigation task by over 30% compared to an unweighted sensor encoding. A hybrid scheme using the HDC weighted scheme at the input of a deep feed-forward neural network achieves the highest success rate. The hybrid scheme furthermore is more robust when reducing the HDC dimension by 50%. However, the simple HDC implementation remains the most hardware efficient, making it desirable for resource-constrained systems.

Body Area Networks (BANs) can perform the task of continuous, remote monitoring of a patient's physiological signals in diverse environments. Apart from providing healthcare professionals with extensive logs of a patient's physiological history, BANs can be used to identify and react to emergency situations. We identify three important factors that afflict wireless communication in BANs: impermeability of the human body to radio waves at frequencies commonly used in BANs, efficient operation in mobile and time-varying environments, and mission-critical requirements for quick response to emergencies. An understanding of the link layer behavior of wireless sensor nodes placed on the body is crucial to address these and other challenges such as reducing energy consumption and increasing network lifetime.

As body sensor networks (BSNs) advance to fulfill the promise of continuous, non-intrusive, remote monitoring of patients, it is important that we achieve efficient communication between energy constrained on-body sensors. An important design choice which has significant impact on achieving this goal is the network architecture. Star architecture has been the natural choice for BSNs due to the short distances between the nodes. In this paper, we revisit this choice by quantitatively studying architecture choices using data from experiments conducted by deploying nodes operating at the 2.4 GHz band on actual human volunteers. We compare the star and multihop architectures to highlight their respective performance characteristics. In particular, we use our data to construct multihop networks with routes that maximize end-to-end packet delivery ratio (PDR) and routes that minimize the average number of retransmissions. Since BSNs span an entire spectrum of applications, each with its unique constraints and requirements, there is no solution that is optimal for all applications. Instead, we show the performance across a variety of metrics and the trade-offs that are achievable. We see that a multihop network minimizing retransmissions has several advantages including having better network lifetime as well as the lowest delay and energy consumption.