Fall detection, particularly critical for high-risk demographics like the elderly, is a key public health concern where timely detection can greatly minimize harm. With the advancements in radio frequency technology, radar has emerged as a powerful tool for human detection and tracking. Traditional machine learning algorithms, such as Support Vector Machines (SVM) and k-Nearest Neighbors (kNN), have shown promising outcomes. However, deep learning approaches, notably Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have outperformed in learning intricate features and managing large, unstructured datasets. This survey offers an in-depth analysis of radar-based fall detection, with emphasis on Micro-Doppler, Range-Doppler, and Range-Doppler-Angles techniques. We discuss the intricacies and challenges in fall detection and emphasize the necessity for a clear definition of falls and appropriate detection criteria, informed by diverse influencing factors. We present an overview of radar signal processing principles and the underlying technology of radar-based fall detection, providing an accessible insight into machine learning and deep learning algorithms. After examining 74 research articles on radar-based fall detection published since 2000, we aim to bridge current research gaps and underscore the potential future research strategies, emphasizing the real-world applications possibility and the unexplored potential of deep learning in improving radar-based fall detection.

The bottleneck in upper limb prosthetic design is the myoelectric control algorithm. Here we studied the clinical readiness of the myoelectric postural control algorithm in a laboratory setting with two trans-radial amputees using a commercially available prosthetic limb system.

Based on years of research establishing the utility of socially assistive robots (SARs) for autism spectrum disorder (ASD) intervention, such robots have now become popular tools, widely used in special education schools, autism care centers, and clinical settings. Most previous studies have explored the roles of SARs as instructors, learning aides, and social-skills trainers, focusing on the learning, language, and social impairments associated with ASD. This article addresses aspects of empathy and emotion regulation (ER) impairments, which are important underlying factors for many atypicalities manifested in ASD. We discuss the design of our robot's emotional capabilities, its emotion-based action library, and the algorithm it uses to regulate a user's emotions. In addition, we describe a user study that evaluates the ER capabilities of an emotionally expressive empathetic agent as well as its capability to prime higher social engagement in a user.

This paper presents a novel continuum robot sheath for use in single-port minimally invasive procedures such as neuroendoscopy in which the sheath is designed to deliver multiple robotic arms. Articulation of the sheath is achieved by using precurved superelastic tubes lining the working channels used for arm delivery. These tubes perform a similar role to push/pull tendons, but can accomplish shape change of the sheath via rotation as well as translation. A kinematic model using Cosserat rod theory is derived which is based on modeling the system as a set of eccentrically aligned precurved tubes constrained along their length by an elastic backbone. The specific case of a two-arm sheath is considered in detail and its relationship to a concentric tube balanced pair is described. Simulation and experiment are used to investigate the concept, map its workspace and to evaluate the kinematic model.

Needle insertion is a critical aspect of many medical treatments, diagnostic methods, and scientific studies, and is considered to be one of the simplest and most minimally invasive medical procedures. Robot-assisted needle steering has the potential to improve the effectiveness of existing medical procedures and enable new ones by allowing increased accuracy through more dexterous control of the needle tip path and acquisition of targets not accessible by straight-line trajectories. In this article, we describe a robot-assisted needle steering system that uses three integrated controllers: a motion planner concerned with guiding the needle around obstacles to a target in a desired plane, a planar controller that maintains the needle in the desired plane, and a torsion compensator that controls the needle tip orientation about the axis of the needle shaft. Experimental results from steering an asymmetric-tip needle in artificial tissue demonstrate the effectiveness of the system and its sensitivity to various environmental and control parameters. In addition, we show an example of needle steering in ex vivo biological tissue to accomplish a clinically relevant task, and highlight challenges of practical needle steering implementation.