论文标题
使用启用椅子的椅子评估下肢的强度
Assessing Lower Limb Strength using Internet-of-Things Enabled Chair
论文作者
论文摘要
该项目描述了机器学习和图像互联网技术在评估接受康复或治疗的个人的下肢强度的应用。具体而言，它试图通过椅子上的传感器来衡量和评估个体的进度，并通过Google GPU Tensorflow Colab处理数据。压力传感器连接到椅子上的各个位置，包括但不限于座位区域，靠背，手静止和腿部。从个人执行静坐过渡和站立式过渡的个人的传感器数据提供了有关椅子上压力分布和振动运动的时间序列数据集。然后可以将数据集和定时信息送入机器学习模型中，以估计运动各个阶段的相对优势。
This project describes the application of the technologies of Machine Learning and Internet-of-Things to assess the lower limb strength of individuals undergoing rehabilitation or therapy. Specifically, it seeks to measure and assess the progress of individuals by sensors attached to chairs and processing the data through Google GPU Tensorflow CoLab. Pressure sensors are attached to various locations on a chair, including but not limited to the seating area, backrest, hand rests, and legs. Sensor data from the individual performing both sit-to-stand transition and stand-to-sit transition provides a time series dataset regarding the pressure distribution and vibratory motion on the chair. The dataset and timing information can then be fed into a machine learning model to estimate the relative strength and weakness during various phases of the movement.
