Depression Behavioral Markers (Long Immobility & Appetite Change) | 抑郁症辅助行为标记（长时间不动、食欲改变）

Using fixed home cameras (bedroom and dining area), the system analyzes the multi-day behavior pattern of elderly people or solo-living individuals, detecting daily lying-in-bed duration (continuous lying > 20 hours per day) and a sharp drop in eating frequency / duration (e.g., daily eating-action count below 50% of personal baseline). When these behavioral changes persist beyond a configured threshold (e.g., 3 days), the system outputs a behavioral-change report to remind family members or community doctors about possible depressive tendency or other health issues. This skill is ONLY a behavioral-observation aid and is NOT a medical diagnostic tool. Application scenarios: solo-living elderly homes, remote mental-health monitoring, community elderly care. The system generates a daily behavior summary and pushes alerts when an abnormal pattern is detected. Skill features: depression in the elderly often presents as decreased activity, reduced appetite, and increased bed time. AI auto-monitoring of these behavior changes can issue early signals before family or doctors notice, supporting timely intervention, reducing suicide risk, and improving quality of life. Can be integrated into home-care cameras or health-management platforms as a practical mental-health monitoring tool. | 通过家庭固定摄像头（卧室和餐厅区域），分析老年人或独居者连续多日的行为模式，检测卧床时长（连续卧床超过20小时/天）以及进食频次/时长骤减（如每日进食动作次数低于历史基线的50%）。当这些行为变化持续超过设定天数（如3天）时，输出行为变化报告，提醒家属或社区医生关注可能存在的抑郁倾向或其他健康问题。该技能仅为行为观察辅助工具，不作为医学诊断依据。应用场景：独居老人家庭、精神健康远程监测、社区养老。系统每日生成行为摘要，当检测到异常行为模式时推送提醒。技能特点：老年人抑郁症常表现为活动减少、食欲下降、卧床时间增多。通过AI自动监测这些行为变化，可在家属或医生尚未察觉时发出早期信号，有助于及时干预，降低自杀风险，改善生活质量。该技能可集成到居家养老摄像头或健康管理平台中，成为精神健康监测的实用工具。

by @18072937735 (smyx-skills)·MIT-0