Dementia and central nervous system degeneration are common problems in aging societies with regard to the number of people affected and total medical expenses. Socially assistive robotic technology has gradually matured; currently, most scholars believe it can be used as companions in long-term care facilities and to work as caregivers alongside staff to improve the social interaction and mental state of older adults and patients with dementia. Therefore, this study measured the effect of the duration of exposure to socially assistive robots in older adults with dementia. Seven databases were searched up to February 2019 through the consultation of appropriate Internet sites and the use of criteria lists recommended by relevant experts. Randomized controlled trials comparing socially assistive robot use with a control group in older adults with dementia and using at least one of the primary outcomes of agitation, depression, and quality of life were included. Thirteen randomized controlled trials were identified from 873 articles, 7 of which were included in the meta-analysis. The pooled effect estimate from 3 trials with 214 participants revealed that the pet-type robot improved patients’ agitation level, with a standardized mean difference of −0.37 (95% CI: −0.64 to −0.10, p < .01) and no heterogeneity (I² = 0%). The results also revealed that length of each session and pet-type robot exposure time per week were associated with reduced depression levels (β = −0.06, Q = 21.213, df = 1, p < .001 and β = −0.019, Q = 7.532, df = 1, p < .01, respectively). However, the results for quality of life were nonsignificant. Pet-type robot systems seem to be a potential activity in long-term care facilities for dementia care. Further research is warranted to establish a comprehensive intervention plan related to the use of pet-type robots.

“Physical restraint” formerly used as a measure of protection for psychiatric patients is now widely used. However, existing studies showed that physical restraint not only has inadequate effect of protection but also has negative effects on residents. To analyzes the impact of educational program on the physical restraint use in long‐term care facilities. Design: A systematic review with meta‐analysis and meta‐regression. Eight databases, including Cochrane Library, ProQuest, PubMed, EMBASE, EBSCO, Web of Science, Ovid Medline and Physiotherapy Evidence Database (PEDro), were searched up to January 2017. Eligible studies were classified by intervention and accessed for quality using the Quality Assessment Tool for quantitative studies. Sixteen research articles were eligible in the final review; 10 randomize control trail studies were included in the analysis. The meta‐analysis revealed that the use of physical restraint was significantly less often in the experimental (education) group (OR = 0.55, 95% CI: 0.39 to 0.78, p < 0.001) compared to the control group. Meta‐regression revealed the period of post education would have decreased the effect of the restraint educational program (β: 0.08, p = 0.002); instead, the longer education period and more times of education would have a stronger effect of reducing the use of physical restraint (β: −0.07, p < 0.001; β: −0.04, p = 0.056). The educational program had an effect on the reduced use of physical restraint. The results of meta‐regression suggest that long‐term care facilities should provide a continuous education program of physical restraint for caregivers.

The arches of the foot play a vital role in cushioning the impact and pressure generated from ground reaction forces due to body weight. Owing to a lack of normal human arch structure, people diagnosed as having flat feet often have discomfort in the soles of their feet. The results may not only cause inappropriate foot pressure distribution on the sole but also further cause foot injuries. This study heavily relies on a homemade foot pressure sensing device equipped with textured insoles of different heights and artificial arches. This was to explore the extent to which the pressure distribution of the foot in people with flat feet could be improved. A further comparison was made of the effects of using the textured insoles with different heights on two different groups of people diagnosed with flat and normal feet respectively. Sixty-five undergraduate and postgraduate volunteers were invited to receive the ink footprint test for measuring their degrees of arch index. Nine of these 65 had 2 flat feet, 3 had a left flat foot, 5 had a right flat foot, and 48 had 2 normal feet. To ensure the same number of subjects in both the control and the experimental groups, 9 of the 48 subjects who had normal feet were randomly selected. In total, 26 subjects (Male: 25, Female: 1; Age: 22 ± 1 years; height: 173.6 ± 2.5 cm; body mass: 68.3 ± 5.4 kg; BMI: 22.6 ± 1.2) were invited to participate in this foot pressure sensing insoles study. The experimental results showed that the use of textured insoles designed with different heights could not effectively improve the plantar pressure distribution and body stability in subjects with flat feet. Conversely, the use of an artificial arch effectively improved the excessive peak in pressure and poor body stability, and alleviated the problem of plantar collapse for patients with flat feet, especially in the inner part of their hallux and forefoot.

Under the delicate control of the brain, people can perform graceful movements through the coordination of muscles, bones, ligaments, and joints. If artificial intelligence can be used to establish a control system that simulates the movements of human arms, it is believed that the application scope of robotic arms in assisting people’s daily life can be greatly increased. The purpose of this study is to build a general system that can use intelligent techniques to assist in the construction of a personalized rehabilitation system. More importantly, this research hopes to establish an intelligent system that can be adjusted according to the needs of the problem domain, that is, the system can move toward the direction of problem-solving through autonomous learning. The artificial neural molecular system (ANM system), developed early in our laboratory, which captured the close structure/function relationship of biological systems, was used. The system was operated on the V-REP (Virtual Robot Experimentation Platform). The results show that the ANM system can use self-learning methods to adjust the start-up time, rotation angle, and the sequence of the motor operation of different motors in order to complete the designated task assignment.

We have entered a new era, “Industry 4.0”, that sees the overall industry marching toward an epoch of man–machine symbiosis and intelligent production. The developers of so-called “intelligent” systems must attempt to seriously take into account all possible situations that might occur in the real world, to minimize unexpected errors. By contrast, biological systems possess comparatively better “adaptability” than man-made machines, as they possess a self-organizing learning that plays an indispensable role. The objective of this study was to apply a malleable learning system to the movement control of a snake-like robot, to investigate issues related to self-organizing dynamics. An artificial neuromolecular (ANM) system previously developed in our laboratory was used to control the movements of an eight-joint snake-like robot (called Snaky). The neuromolecular model is a multilevel neural network that abstracts biological structure–function relationships into the system’s structure, in particular into its intraneuronal structure. With this feature, the system possesses structure richness in generating a broad range of dynamics that allows it to learn how to complete the assigned tasks in a self-organizing manner. The activation and rotation angle of each motor are dependent on the firing activity of neurons that control the motor. An evolutionary learning algorithm is used to train the system to complete the assigned tasks. The key issues addressed include the self-organizing learning capability of the ANM system in a physical environment. The experimental results show that Snaky was capable of learning in a continuous manner. We also examined how the ANM system controlled the angle of each of Snaky’s joints, to complete each assigned task. The result might provide us with another dimension of information on how to design the movement of a snake-like robot.