Introduction： The senior population is projected to continue to increase dramatically for the foreseeable future and cognitive issues associated with aging have become a major concern as they affect people’s abilit...Introduction： The senior population is projected to continue to increase dramatically for the foreseeable future and cognitive issues associated with aging have become a major concern as they affect people’s ability to carry on activities of daily living. One area of daily living, which has often been cited as a key problem area for seniors is the detection of risks in the home, especially in the kitchen. The kitchen is the place where most domestic accidents occur and the oven is the main source. Methods： We propose a safety kitchen solution, InOvUS, which focuses on safety and reducing the risk of fire, burn and intoxication. We present the evaluation of the soundness of the method we designed to evaluate the adoption intention and interest of a safety kitchen system from a senior user’s perspective. Results： We develop a conceptual model utilizing several existing scales such as the CAI （consumer adoption intention）, CI （consumer innovativeness）, TAM （technology acceptance model）, PEOU （perceived ease of use） and PU （perceived usefulness） scales, but specific to the senior 65＋ segment. Conclusion： The evaluation results of InOvUS through the application of our model show a clear buying intention toward InOvUS and also a clear intent to use it.展开更多
To date, there has been limited research carried out to better understand seniors＇ needs and purchase motivations related to mobile devices. To that end, this research enabled an exploratory assessment of the intrins...To date, there has been limited research carried out to better understand seniors＇ needs and purchase motivations related to mobile devices. To that end, this research enabled an exploratory assessment of the intrinsic and extrinsic needs/motives to consider in future research and development of ubiquitous mobile devices and related applications, specifically for seniors. The 65＋ population is expected to double by 2025 （WHO, 2013） from 390 million to 800 million. The results demonstrate specific needs/motives which should be considered during the development of new mobile attributes and apps for this segment. For both attributes of devices themselves and the applications found on them, three tiers of priority for development were determined.展开更多
The quality of sleep may be a reflection of an el- derly individual＇s health state, and sleep pattern is an im- portant measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly...The quality of sleep may be a reflection of an el- derly individual＇s health state, and sleep pattern is an im- portant measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novel multi-parametric sensing system called sleep pattern recognition system （SPRS）. This system, equipped with a combination of various non-invasive sensors, can mon- itor an elderly user＇s sleep behavior. It accumulates the de- tecting data from a pressure sensor matrix and ultra wide band （UWB） tags. Based on these two types of complemen- tary sensing data, SPRS can assess the user＇s sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operates without disrupting the users＇ sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.展开更多
文摘Introduction： The senior population is projected to continue to increase dramatically for the foreseeable future and cognitive issues associated with aging have become a major concern as they affect people’s ability to carry on activities of daily living. One area of daily living, which has often been cited as a key problem area for seniors is the detection of risks in the home, especially in the kitchen. The kitchen is the place where most domestic accidents occur and the oven is the main source. Methods： We propose a safety kitchen solution, InOvUS, which focuses on safety and reducing the risk of fire, burn and intoxication. We present the evaluation of the soundness of the method we designed to evaluate the adoption intention and interest of a safety kitchen system from a senior user’s perspective. Results： We develop a conceptual model utilizing several existing scales such as the CAI （consumer adoption intention）, CI （consumer innovativeness）, TAM （technology acceptance model）, PEOU （perceived ease of use） and PU （perceived usefulness） scales, but specific to the senior 65＋ segment. Conclusion： The evaluation results of InOvUS through the application of our model show a clear buying intention toward InOvUS and also a clear intent to use it.
文摘To date, there has been limited research carried out to better understand seniors＇ needs and purchase motivations related to mobile devices. To that end, this research enabled an exploratory assessment of the intrinsic and extrinsic needs/motives to consider in future research and development of ubiquitous mobile devices and related applications, specifically for seniors. The 65＋ population is expected to double by 2025 （WHO, 2013） from 390 million to 800 million. The results demonstrate specific needs/motives which should be considered during the development of new mobile attributes and apps for this segment. For both attributes of devices themselves and the applications found on them, three tiers of priority for development were determined.
文摘The quality of sleep may be a reflection of an el- derly individual＇s health state, and sleep pattern is an im- portant measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novel multi-parametric sensing system called sleep pattern recognition system （SPRS）. This system, equipped with a combination of various non-invasive sensors, can mon- itor an elderly user＇s sleep behavior. It accumulates the de- tecting data from a pressure sensor matrix and ultra wide band （UWB） tags. Based on these two types of complemen- tary sensing data, SPRS can assess the user＇s sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operates without disrupting the users＇ sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.
