Elderly Gait Instability / Shuffling Step Detection | 老年人步态不稳/小碎步识别

Using a fixed camera in a hallway or living room to record video of an elderly person walking in a straight line, AI pose estimation and gait analysis extract parameters such as step length (cm), gait speed (m/s), trunk sway angle (left-right tilt), and cadence to evaluate gait stability. When step length is too small (small-shuffling steps), gait speed is too slow, or trunk sway is too large, the system outputs a fall risk level (low / medium / high). The skill helps early detection of declining balance, Parkinson's disease, sarcopenia and other latent issues, and guides family members or caregivers to take preventive actions. Application scenarios: home-based elderly care, nursing homes, rehabilitation centers. The system can be scheduled (e.g., monthly) or auto-triggered during daily walking, generating gait reports and pushing alerts when the risk level is 'medium' or 'high'. Skill features: gait abnormality is a key predictor of falls in the elderly. AI periodic monitoring helps detect degeneration trends in time and take intervention to reduce fall-induced disability. Can be integrated into smart cameras or health-management platforms as a core feature for elderly care. | 通过走廊或客厅的固定摄像头拍摄老年人直线行走的视频，利用AI姿态估计和步态分析技术检测步幅长度（cm）、步速（m/s）、躯干摇摆角度（左右倾斜度）以及步频等参数，评估步态稳定性。当步幅过小（小碎步）、步速过慢、躯干摇摆幅度过大时，输出跌倒风险等级（低/中/高）。该技能有助于早期发现老年人平衡能力下降、帕金森病、肌少症等潜在问题，指导家属或护理人员采取预防措施。应用场景：居家养老、养老院、康复中心。系统定期（如每月）或在老年人日常行走时自动触发检测，生成步态报告，当风险等级为'中'或'高'时推送提醒。技能特点：步态异常是老年人跌倒的重要预测因子。通过AI定期监测，可及早发现退化趋势，采取干预措施，降低跌倒致残率。该技能可集成到智能摄像头或健康管理平台中，成为养老监护的核心功能。

by @smyx-sunjinhui·MIT-0