论文标题
检测门猛烈抨击以监视家庭暴力迹象的可行性
Feasibility on Detecting Door Slamming towards Monitoring Early Signs of Domestic Violence
论文作者
论文摘要
通过使用低成本的微控制器和Tinyml，我们研究了检测家庭中家庭暴力和其他反社会行为的潜在预警信号的可行性。我们创建了一个机器学习模型，以确定是否通过分析音频数据并将其馈入卷积神经网络以对样本进行分类，从而确定门是否积极关闭。在没有背景噪声的测试条件下，达到88.89 \％的精度，当混合了各种背景噪声的相对体积的0.5倍时，降至87.50 \％。然后，将模型部署在门上附有的Arduino纳米BLE 33中，只有在检测到大于预定义阈值加速度的加速度后才开始采样。然后可以通过BLE将模型的预测发送到另一台设备，例如Raspberry Pi的智能手机。
By using low-cost microcontrollers and TinyML, we investigate the feasibility of detecting potential early warning signs of domestic violence and other anti-social behaviors within the home. We created a machine learning model to determine if a door was closed aggressively by analyzing audio data and feeding this into a convolutional neural network to classify the sample. Under test conditions, with no background noise, accuracy of 88.89\% was achieved, declining to 87.50\% when assorted background noises were mixed in at a relative volume of 0.5 times that of the sample. The model is then deployed on an Arduino Nano BLE 33 Sense attached to the door, and only begins sampling once an acceleration greater than a predefined threshold acceleration is detected. The predictions made by the model can then be sent via BLE to another device, such as a smartphone of Raspberry Pi.
