  Human activity analysis based on sensor data plays a significant role in
behavior sensing, human-machine interaction, health care, and so on. The
current research focused on recognizing human activity and posture at the
activity pattern level, neglecting the effective fusion of multi-sensor data
and assessing different movement styles at the individual level, thus
introducing the challenge to distinguish individuals in the same movement. In
this study, the concept of RunnerDNA, consisting of five interpretable
indicators, balance, stride, steering, stability, and amplitude, was proposed
to describe human activity at the individual level. We collected smartphone
multi-sensor data from 33 volunteers who engaged in physical activities such as
walking, running, and bicycling and calculated the data into five indicators of
RunnerDNA. The indicators were then used to build random forest models and
recognize movement activities and the identity of users. The results show that
the proposed model has high accuracy in identifying activities (accuracy of
0.679) and is also effective in predicting the identity of running users.
Furthermore, the accuracy of the human activity recognition model has
significant improved by combing RunnerDNA and two motion feature indicators,
velocity, and acceleration. Results demonstrate that RunnerDNA is an effective
way to describe an individual's physical activity and helps us understand
individual differences in sports style, and the significant differences in
balance and amplitude between men and women were found.
