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传感、识别与安全 / 步态跌倒与移动监测

医生难以利用可穿戴设备数据准确评估患者在现实场景中的步态变化。

lightbulb现实痛点步态跌倒与移动监测

医生难以利用可穿戴设备数据准确评估患者在现实场景中的步态变化。

CHI '22'Are They Doing Better In The Clinic Or At Home': Understanding Clinicians? Needs When Visualizing Wearable Sensor Data Used In Remote Gait Assessments For People With Multiple Sclerosis

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lightbulb现实痛点步态跌倒与移动监测

患者在家中难以采集长时直线步态数据，难以进行准确的步态周期分割。

UbiComp '24GCCRR: A Short Sequence Gait Cycle Segmentation Method Based on Ear-Worn IMU

lightbulb现实痛点步态跌倒与移动监测

膝关节炎患者难以便捷地监测步态中的关节负担和肌肉力量。

UbiComp '24KneeGuard: A Calibration-free Wearable Monitoring System for Knee Osteoarthritis Gait Re-training via Effortless Wearing

lightbulb现实痛点步态跌倒与移动监测

现有手环在推购物车或随机手臂动作时无法准确数步。

UbiComp '24Earable and Wrist-worn Setup for Accurate Step Counting Utilizing Body-Area Electrostatic Sensing

lightbulb现实痛点步态跌倒与移动监测

室内步态识别系统部署复杂，精度受环境影响大。

UbiComp '24RDGait: A mmWave Based Gait Recognition System for Complex Indoor Environments Using Single-chip Radar

lightbulb现实痛点步态跌倒与移动监测

用户难以用便捷低成本的方法评估自身脑健康风险。

UbiComp '23NeuralGait: Assessing Brain Health Using Your Smartphone

lightbulb现实痛点步态跌倒与移动监测

老人在跌倒后若不能及时获救可能面临生命危险。

UbiComp '23LT-Fall: The Design and Implementation of a Life-threatening Fall Detection and Alarming System

相关论文

RDGait：基于单芯片雷达的复杂室内环境毫米波步态识别系统

Dequan Wang, Xinran Zhang, Kai Wang

NeuralGait：使用智能手机评估大脑健康

Huining Li, Huan Chen, Chenhan Xu

LT-Fall：危及生命的跌倒检测与报警系统的设计与实现

Duo Zhang, Xusheng Zhang, Shengjie Li

'他们在诊所还是在家表现更好': 理解临床医生在可视化远程步态评估中可穿戴传感器数据时的需求，针对多发性硬化症患者

Ayanna Seals, Giuseppina Pilloni, Jin Kim