Companion robots hold immense potential in providing emotional support to older adults in the rapidly aging world. However, questions have been raised regarding whether healthy older adults benefit from having a robotic companion, how they perceive the value of companion robots, and what their relationship with companion robots would be like. To understand healthy older adults' perceptions, attitudes, and relationships toward companion robots, we conducted multiple focus groups with eighteen retirees. Our findings reveal the social context encountered by older adults in China and the mismatch between the current value proposition of companion robots and healthy older adults' needs. We further identify factors that may influence the adoption of robotic companionship, which include individuals' self-disclosure tendencies, quality of companionship, differentiated value, and seamless collaboration with aging-in-community infrastructure and services.

With the rapid development of artificial intelligence, particularly the rise of deep learning, the importance of Explainable Artificial Intelligence has become increasingly prominent. Among its key techniques, counterfactual explanation plays a crucial role in understanding the decision-making mechanisms of opaque models. However, the high dimensionality and complex feature patterns of image data pose significant challenges for the task of generating counterfactuals for images. Existing literature has proposed various algorithms based on different assumptions, many of which rely on the existence of appropriate generative models. Some of these assumptions, particularly the assumption regarding the existence of generative models, may be overly stringent. To address this issue, this letter introduces a novel assumption-free image counterfactual generation algorithm, DFO-S, based on Score Matching and gradient-free optimization techniques. The proposed method achieves high-quality counterfactual generation without relying on generative models. Through extensive empirical analysis, we demonstrate the significant superiority of our method in terms of performance.

This paper presents a high-efficiency, wide-input power range ambient Radio Frequency Energy Harvester (RFEH) system, which comprises an adaptive 4-mode impedance matching network (IMN), a single-stage cross-coupled differential-drive (CCDD) rectifier, a maximum power point tracking (MPPT) circuit, and a power manager unit (PMU). To adaptively configure the impedance matching network and modulate the TONof the buck-boost converter, a feedback-free fast (F3) MPPT technique based on the input power detection and parameters direct adjustment is proposed. It can effectively address the trade-off between the speed and accuracy in the impedance matching as well as in the maximum power transmission. Therefore, the power conversion efficiency (PCE) of the proposed RFEH system has been greatly improved. The power manager unit (PMU) is composed of a buck-boost converter, a self-supply unit, and a load regulator, which is employed to implement the F3MPPT, startup, and self-powered of the RFEH system. The proposed design is fabricated in a 0.18-$\mu $m CMOS technology, and the chip area is about$1.52\times 0.78$mm2. The measurement results demonstrate that the proposed RFEH system can operate in a wide PINrange of −23 to 1 dBm with over 96% MPPT accuracy. It achieves PCE >20% in the PINrange of −17 dBm to 1 dBm, with a peak PCE of 49.4% at P$_{\mathrm {IN}}= -5$dBm.

Rehabilitation therapies are widely employed to assist people with motor impairments in regaining control over their affected body parts. Nevertheless, factors such as fatigue and low self-efficacy can hinder patient compliance during extensive rehabilitation processes. Utilizing hand redirection in virtual reality (VR) enables patients to accomplish seemingly more challenging tasks, thereby bolstering their motivation and confidence. While previous research has investigated user experience and hand redirection among able-bodied people, its effects on motor-impaired people remain unexplored. In this paper, we present a VR rehabilitation application that harnesses hand redirection. Through a user study and semi-structured interviews, we examine the impact of hand redirection on the rehabilitation experiences of people with motor impairments and its potential to enhance their motivation for upper limb rehabilitation. Our findings suggest that patients are not sensitive to hand movement inconsistency, and the majority express interest in incorporating hand redirection into future long-term VR rehabilitation programs.