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.

利用深度学习架构从人体传感器数据中评估人物和柔性生物识别识别性能，并发现活动对此有影响，同时软生物识别属性表征表现出良好的性能。