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.
翻译：我们通过使用低成本微控制器和微微控制器,调查在家庭内发现家庭暴力和其他反社会行为的潜在预警迹象的可行性。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将门积极关闭,并将它输入一个革命性神经网络对样本进行分类。在测试条件下,没有背景噪音,实现了88.89 ⁇ 的准确度,当各种背景噪音混杂在一起,其数量比样本多0.5倍时,该模型就下降到87.50 ⁇ 。该模型随后安装在附属于门的Arduino Nano Nio MLE 33 Sense上,并且只有在检测到比预先确定的临界加速度更大的加速度时才开始取样。然后,模型所作的预测可以通过白莓手机发送到另一个装置,例如Raspberry Pi的智能手机。
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