Abstract：
Researchers face the challenge of defining subject selection criteria when training algorithms for human activity recognition tasks. The ongoing uncertainty revolves around which characteristics should be considered to ensure algorithmic robustness across diverse populations. This study aims to address this challenge by conducting an analysis of heterogeneity in the training data to assess the impact of physical characteristics and soft-biometric attributes on activity recognition performance. The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis. Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects\' characteristics on activity recognition performance, providing valuable insights into the algorithm\'s robustness across diverse populations. This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.
摘要：
研究人员在训练人类活动识别任务的算法时面临着定义主题选择标准的挑战。持续的不确定性围绕着应考虑哪些特征来确保跨不同群体的算法鲁棒性。本研究旨在通过对训练数据中的异质性进行分析来评估物理特征和软生物特征属性对活动识别性能的影响来解决这一挑战。各种最先进的深度神经网络架构的性能(tCNN，hybrid-LSTM,变压器模型)使用IntelliRehab(IRDS)数据集处理时间序列数据进行评估。通过根据人类特征在训练数据中故意引入偏见，目的是识别影响运动分析算法的特征。实验结果表明，CNN-LSTM模型取得了最高的精度，达到88%。此外，在残疾属性的异质分布上训练的模型表现出明显更高的准确性，达到51%,与那些不考虑这些因素的人相比，平均得分为33%。这些评估强调了受试者特征对活动识别表现的显著影响，为算法在不同群体中的鲁棒性提供有价值的见解。这项研究通过量化医疗保健领域内多通道时间序列活动识别数据中的表示偏差，在促进人工智能的公平性和可信度方面迈出了重要一步。
