Multi-channel time-series datasets are popular in the context of human activity recognition (HAR). On-body device (OBD) recordings of human movements are often preferred for HAR applications not only for their reliability but as an approach for identity protection, e.g., in industrial settings. Contradictory, the gait activity is a biometric, as the cyclic movement is distinctive and collectable. In addition, the gait cycle has proven to contain soft-biometric information of human groups, such as age and height. Though general human movements have not been considered a biometric, they might contain identity information. This work investigates person and soft-biometrics identification from OBD recordings of humans performing different activities using deep architectures. Furthermore, we propose the use of attribute representation for soft-biometric identification. We evaluate the method on four datasets of multi-channel time-series HAR, measuring the performance of a person and soft-biometrics identification and its relation concerning performed activities. We find that person identification is not limited to gait activity. The impact of activities on the identification performance was found to be training and dataset specific. Soft-biometric based attribute representation shows promising results and emphasis the necessity of larger datasets.

翻译：多通道时间序列数据集在人类活动识别（HAR）领域广泛应用。人体附着设备（OBD）记录的人体运动数据因其可靠性和身份保护特性（如工业场景）而成为HAR应用的首选。然而矛盾的是，步态活动本身具有生物特征属性——其周期性运动既具独特性又易被采集。此外，步态周期已被证实包含人类群体的软生物特征信息（如年龄、身高）。虽然一般性人体运动尚未被视为生物特征，但其可能蕴含身份信息。本研究利用深度架构，基于执行不同活动的人体OBD记录实现人物与软生物特征识别。我们进一步提出采用属性表征方法进行软生物特征识别。在四个多通道时间序列HAR数据集上评估该方法，衡量人物与软生物特征识别性能及其与执行活动间的关联性。实验发现人物识别并不局限于步态活动；活动对识别性能的影响具有训练与数据集特异性。基于软生物特征的属性表征展现出良好效果，同时凸显了构建更大规模数据集的必要性。