Three-dimensional hand gesture recognition has attracted increasing research interests in computer vision, pattern recognition, and human-computer interaction. The emerging depth sensors greatly inspired various hand gesture recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand gesture recognition using 3D depth sensors. We first review the commercial depth sensors and public data sets that are widely used in this field. Then, we review the state-of-the-art research for 3D hand gesture recognition in four aspects: 1) 3D hand modeling; 2) static hand gesture recognition; 3) hand trajectory gesture recognition; and 4) continuous hand gesture recognition. While the emphasis is on 3D hand gesture recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.

Survey on 3D Hand Gesture Recognition

Although an Internet-of-Things-based smart home solution can provide an improved and better approach to healthcare management, yet its end user adoption is very low. With elderly people as the main target, these conservative users pose a serious challenge to the successful implementation of smart home healthcare services. The objective of this research was to develop and test a theoretical framework empirically for determining the core factors that can affect the elderly users’ acceptance of smart home services for healthcare. Accordingly, an online survey was conducted with 254 elderly people aged 55 years and above across four Asian countries. Partial least square structural equation modeling was applied to analyze the effect of eight hypothesized predicting constructs. The user perceptions were measured on a conceptual level rather than the actual usage intention toward a specific service. Performance expectancy, effort expectancy, expert advice, and perceived trust have a positive impact on the behavioral intention. The same association is negative for technology anxiety and perceived cost. Facilitating conditions and social influence do not have any effect on the behavioral intention. The model could explain 81.4% of the total variance in the dependent variable i.e., behavioral intention. Effort expectancy is the leading predictor of smart homes for healthcare acceptance among the elderly. Together with expert advice, perceived trust, and perceived cost, these four factors represent the key influence of the elderly peoples’ acceptance behavior. This paper provides the groundwork to explore the process of the actual adoption of smart home services for healthcare by the elderly people with potential future research areas.

Internet-of-Things and Smart Homes for Elderly Healthcare: An End User Perspective

Ethics of healthcare robotics: Towards responsible research and innovation

RiSH: A robot-integrated smart home for elderly care

In recent years, with the current advancements in Robotics and Artificial Intelligence (AI), robots have the potential to support the field of healthcare. Robotic systems are often introduced in the care of the elderly, children, and persons with disabilities, in hospitals, in rehabilitation and walking assistance, and other healthcare situations. In this survey paper, the recent advances in robotic technology applied in the healthcare domain are discussed. The paper provides detailed information about state-of-the-art research in care, hospital, assistive, rehabilitation, and walking assisting robots. The paper also discusses the open challenges healthcare robots face to be integrated into our society.

Stefan Hofmann

Papers

Hobbit, a care robot supporting independent living at home