No abstract

This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate.

No abstract

Group conversation, one form of social activities, plays main roles to train and rehabilitate cognitive function as well as improve emotional states in older adults. It has been mainly utilized for healthy older adults. This paper proposes a robot that warms up group conversations of older adults by reusing or repeating speech statements, which are played successfully to activate group conversations of older adults previously. A novel group conversation technique called the "coimagination" method for preventing mild cognitive impairments and dementia, was used for collecting and reusing conversation data. Two types of group conversation experiments were conducted among older adults. 1) All participants who were human in coimagination sessions, present their original stories with pictures according to selected topic. 2) One of participants in coimagination sessions was a robot, which presents the reproduced interesting stories. These reproduced stories were collected and implemented into the robot in advance. We analyzed the data by the frequency of evoked laughter in each topic and in all participants. The reproduced stories presented by the robot created more laughter than the original stories presented by human. The robot successfully elicited more laughter than the human participants. Based on these results, we found that the robot successfully enlivened group conversation through evoking laughter.

Diabetes mellitus (DM) or hyperglycemia (in a more generalized term, high blood sugar) is a metabolic disorder that is now highly prevalent in the world population. Most of the food that people consume is converted into glucose, which enters the bloodstream following absorption–assimilation mechanisms. As a natural process, cells in our body utilize glucose for growth and energy. The glucose balance is maintained by a hormone called insulin that is secreted by the beta cells of pancreas. Hypotheses at the backdrop of DM occurrence are either (i) enough insulin is not produced and secreted resulting in increased level of glucose in blood, or (ii) insulin is insensitive to glucose, or (iii) insulin is non-targeted etc. If DM remains uncontrolled over time, it leads to serious damage to many of the body's systems, especially the nerves and blood vessels. This paper develops an enquiry into diabetes from many angles: (i) Diabetes as a disorder, its complications, causes, diagnostic tests, and treatment; (ii) Analysis of retinal and plantar images to characterize diabetes complications; (iii) How analysis of heart rate variability signals can depict diabetes; (iv) Biomedical engineering of the glucose–insulin regulatory system, and its employment in the modeling of the oral glucose tolerance test data, to detect diabetes as well as persons at risk of being diabetic; (v) Application of the glucose–insulin regulatory system to formulate an insulin delivery system for controlling blood sugar.