首页|New Machine Learning Study Findings Have Been Reported by Investigators at Shanghai Jiao Tong University (Understanding Complex Interactions Between Neighborhood Environment and Personal Perception In Affecting Walking Behavior of Older Adults: ...)
New Machine Learning Study Findings Have Been Reported by Investigators at Shanghai Jiao Tong University (Understanding Complex Interactions Between Neighborhood Environment and Personal Perception In Affecting Walking Behavior of Older Adults: ...)
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NETL
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Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learning have been published. According to news originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “The population aging is a growing problem worldwide. Walking is one of the most important ways of self management of health for older adults, determined by many factors, such as neighborhood environment (NE) and socio-economic attributes.” Financial supporters for this research include National Social Science Fund, Shanghai Municipal Bureau of Planning and Natural Resources Fund. Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, “Al- though the previous studies have typically predicted elderly walking behavior through NE, they are limited by the methodological system and data collection, resulting in low prediction accuracy. To this end, this study incorporates residents’ subjective perceptions of the environment and objective neighborhood envi- ronmental attributes into the evaluation system, uses human-machine adversarial framework and machine learning methods to predict elderly walking behavior, and assesses the nonlinear effects of each factor. The results show that (1) combining subjective and objective factors, the prediction accuracy of elderly walk- ing behavior has been effectively improved based on human-machine adversarial framework and machine learning methods. (2) The nonlinear and threshold effects of environmental and perceptual factors on the walking time of the elderly were revealed. (3) The neighborhood attributes were incorporated into the walking behavior prediction, and were found to be of comparable importance to the influence of the NE on the behavior of the elderly.”
ShanghaiPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningShanghai Jiao Tong University
NETL
NSTL
Elsevier
