Elderly Loneliness / Depression-Tendency Behavior Analysis | 老年人孤独/抑郁倾向行为分析

Using fixed cameras at home (living room, bedroom) of elderly people living alone, the system analyzes daily videos and detects negative behavior indicators during solo time: dazing (long-duration motionless gazing without purposeful action), sighing (rapid chest rise-and-fall with audible expiration), and self-talking (mouth movement without any conversation partner). It counts the frequency and duration of these behaviors and comprehensively evaluates the elder's emotional risk level (low / medium / high). The skill assists family members or community workers in understanding the elder's mental state and timely providing emotional care or psychological intervention. Application scenarios: homes of solo-living elders, nursing homes, community daycare centers. The system generates a daily emotional-risk report; when the risk level is 'medium' or 'high', it pushes reminders. Skill features: loneliness and depression in the elderly are common mental-health issues, and early behavioral signals are often overlooked. AI automatic monitoring of dazing / sighing / self-talking helps family members detect mental abnormalities early, intervene promptly, and improve the elder's quality of life. Can be integrated into home-care cameras or community health-management platforms. | 通过独居老人在家中的固定摄像头（如客厅、卧室），分析日常视频，检测独处期间的消极行为指标：发呆（长时间静止注视，缺乏目的性动作）、叹气（胸部快速起伏伴呼气声）、自言自语（口部活动但无对话对象）等。统计这些行为的发生频次和持续时间，综合评估老年人潜在的情绪风险等级（低/中/高）。该技能可辅助家属或社区工作者了解老人心理状态，及时进行情感关怀或心理干预。应用场景：独居老人家庭、养老院、社区日间照料中心。系统每日生成情绪风险报告，当风险等级为'中'或'高'时推送提醒。技能特点：老年人孤独和抑郁是常见的心理健康问题，早期行为信号常被忽视。通过AI自动监测发呆、叹气、自言自语等行为，可辅助家属及早发现心理异常，及时干预，提高老年人生活质量。该技能可集成到居家养老摄像头或社区健康管理平台中。

by @smyx-sunjinhui·MIT-0