James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh EH14 4AS, UK.
Sensors (Basel). 2020 Dec 17;20(24):7226. doi: 10.3390/s20247226.
With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems. Therefore, by exploiting the associative memory feature of Hopfield Neural Network, a hardware module has been designed to simulate the Neural Network algorithm which uses sensor data integration and data classification for recognizing the fall. By adopting the Hebbian learning method for training neural networks, weights of human activity features are obtained and implemented/embedded into the hardware design. Here, the neural network weight of fall activity is achieved through data preprocessing, and then the weight is mapped to the amplification factor setting in the hardware. The designs are checked with validation scenarios, and the experiment is completed with a Hopfield neural network in the analog module. Through simulations, the classification accuracy of the fall data reached 88.9% which compares well with some other results achieved by the software-based machine-learning algorithms, which verify the feasibility of our hardware design. The designed system performs the complex signal calculations of the hardware's feedback signal, replacing the software-based method. A straightforward circuit design is used to meet the weight setting from the Hopfield neural network, which is maximizing the reusability and flexibility of the circuit design.
随着智能可穿戴系统的普及，传感器信号处理对嵌入式场景中的机器学习提出了更多挑战。例如，用于数据分类的传统机器学习方法，特别是在实时情况下，计算量很大。在嵌入式硬件上部署人工智能算法以实现快速数据分类和准确的跌倒检测，这对实现节能的嵌入式系统来说是一个巨大的挑战。因此，通过利用 Hopfield 神经网络的联想记忆特性，设计了一个硬件模块来模拟神经网络算法，该算法使用传感器数据集成和数据分类来识别跌倒。通过采用赫布学习方法对神经网络进行训练，获得人类活动特征的权重，并将其实现/嵌入到硬件设计中。在这里，通过数据预处理获得跌倒活动的神经网络权重，然后将权重映射到硬件中的放大系数设置。通过验证场景检查设计，并在模拟模块中使用 Hopfield 神经网络完成实验。通过仿真，跌倒数据的分类准确性达到 88.9%，与基于软件的机器学习算法的一些其他结果相比表现良好，验证了我们硬件设计的可行性。设计的系统执行硬件反馈信号的复杂信号计算，取代了基于软件的方法。使用直截了当的电路设计来满足 Hopfield 神经网络的权重设置，这最大限度地提高了电路设计的可重用性和灵活性。
