PDF(Pubmed)
Abstract：
BACKGROUND: Monitoring the lifestyles of older adults helps promote independent living and ensure their well-being. The common technologies for home monitoring include wearables, ambient sensors, and smart household meters. While wearables can be intrusive, ambient sensors require extra installation, and smart meters are becoming integral to smart city infrastructure. Research Gap: The previous studies primarily utilized high-resolution smart meter data by applying Non-Intrusive Appliance Load Monitoring (NIALM) techniques, leading to significant privacy concerns. Meanwhile, some Japanese power companies have successfully employed low-resolution data to monitor lifestyle patterns discreetly.
METHODS: This study develops a lifestyle monitoring system for older adults using low-resolution smart meter data, mapping electricity consumption to appliance usage. The power consumption data are collected at 15-min intervals, and the background power threshold distinguishes between the active and inactive periods (0/1). The system quantifies activity through an active score and assesses daily routines by comparing these scores against the long-term norms. Key Outcomes/Contributions: The findings reveal that low-resolution data can effectively monitor lifestyle patterns without compromising privacy. The active scores and regularity assessments calculated using correlation coefficients offer a comprehensive view of residents\' daily activities and any deviations from the established patterns. This study contributes to the literature by validating the efficacy of low-resolution data in lifestyle monitoring systems and underscores the potential of smart meters in enhancing elderly people\'s care.
METHODS: This study develops a lifestyle monitoring system for older adults using low-resolution smart meter data, mapping electricity consumption to appliance usage. The power consumption data are collected at 15-min intervals, and the background power threshold distinguishes between the active and inactive periods (0/1). The system quantifies activity through an active score and assesses daily routines by comparing these scores against the long-term norms. Key Outcomes/Contributions: The findings reveal that low-resolution data can effectively monitor lifestyle patterns without compromising privacy. The active scores and regularity assessments calculated using correlation coefficients offer a comprehensive view of residents\' daily activities and any deviations from the established patterns. This study contributes to the literature by validating the efficacy of low-resolution data in lifestyle monitoring systems and underscores the potential of smart meters in enhancing elderly people\'s care.
摘要：
背景：监测老年人的生活方式有助于促进独立生活并确保他们的福祉。家庭监控的常见技术包括可穿戴设备，环境传感器,和智能家用电表。虽然可穿戴设备可能是侵入性的，环境传感器需要额外安装，智能电表正成为智慧城市基础设施的组成部分。研究差距：先前的研究主要通过应用非侵入式设备负载监测（NIALM）技术来利用高分辨率智能电表数据，导致重大隐私问题。同时,一些日本电力公司已经成功地利用低分辨率数据来谨慎地监测生活方式。
方法：这项研究使用低分辨率智能电表数据为老年人开发了一种生活方式监测系统，将用电量映射到电器使用量。以15分钟为间隔收集功耗数据，和背景功率阈值区分活动和非活动时段(0/1)。该系统通过主动评分量化活动，并通过将这些评分与长期规范进行比较来评估日常工作。主要结果/贡献：研究结果表明，低分辨率数据可以在不影响隐私的情况下有效监控生活方式。使用相关系数计算的活跃得分和规律性评估提供了居民日常活动以及与既定模式的任何偏差的全面视图。本研究通过验证低分辨率数据在生活方式监测系统中的功效，为文献做出了贡献，并强调了智能电表在增强老年人护理方面的潜力。
方法：这项研究使用低分辨率智能电表数据为老年人开发了一种生活方式监测系统，将用电量映射到电器使用量。以15分钟为间隔收集功耗数据，和背景功率阈值区分活动和非活动时段(0/1)。该系统通过主动评分量化活动，并通过将这些评分与长期规范进行比较来评估日常工作。主要结果/贡献：研究结果表明，低分辨率数据可以在不影响隐私的情况下有效监控生活方式。使用相关系数计算的活跃得分和规律性评估提供了居民日常活动以及与既定模式的任何偏差的全面视图。本研究通过验证低分辨率数据在生活方式监测系统中的功效，为文献做出了贡献，并强调了智能电表在增强老年人护理方面的潜力。
