Laboratory of Bioengineering, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.
Sensors (Basel). 2022 Mar 29;22(7):2609. doi: 10.3390/s22072609.
Human Action Recognition (HAR) is a rapidly evolving field impacting numerous domains, among which is Ambient Assisted Living (AAL). In such a context, the aim of HAR is meeting the needs of frail individuals, whether elderly and/or disabled and promoting autonomous, safe and secure living. To this goal, we propose a monitoring system detecting dangerous situations by classifying human postures through Artificial Intelligence (AI) solutions. The developed algorithm works on a set of features computed from the skeleton data provided by four Kinect One systems simultaneously recording the scene from different angles and identifying the posture of the subject in an ecological context within each recorded frame. Here, we compare the recognition abilities of Multi-Layer Perceptron (MLP) and Long-Short Term Memory (LSTM) Sequence networks. Starting from the set of previously selected features we performed a further feature selection based on an SVM algorithm for the optimization of the MLP network and used a genetic algorithm for selecting the features for the LSTM sequence model. We then optimized the architecture and hyperparameters of both models before comparing their performances. The best MLP model (3 hidden layers and a Softmax output layer) achieved 78.4%, while the best LSTM (2 bidirectional LSTM layers, 2 dropout and a fully connected layer) reached 85.7%. The analysis of the performances on individual classes highlights the better suitability of the LSTM approach.
人体动作识别（HAR）是一个快速发展的领域，影响着许多领域，其中包括环境辅助生活（AAL）。在这种情况下，HAR 的目的是满足脆弱个体的需求，无论他们是老年人和/或残疾人，并促进自主、安全和有保障的生活。为此，我们提出了一个监测系统，通过人工智能（AI）解决方案对人体姿势进行分类，从而检测危险情况。开发的算法基于从四个 Kinect One 系统同时记录场景的不同角度的骨骼数据计算的一组特征，在每个记录的帧中识别出主体的姿势。在这里，我们比较了多层感知机（MLP）和长短时记忆（LSTM）序列网络的识别能力。从之前选择的特征集中，我们基于 SVM 算法进行了进一步的特征选择，用于优化 MLP 网络，并使用遗传算法选择 LSTM 序列模型的特征。然后，我们优化了这两个模型的架构和超参数，然后比较了它们的性能。最好的 MLP 模型（3 个隐藏层和一个 Softmax 输出层）达到了 78.4%，而最好的 LSTM（2 个双向 LSTM 层、2 个 dropout 和一个全连接层）达到了 85.7%。对个别类别的性能分析突出了 LSTM 方法的更好适用性。
