Abstract:
Human gait enables information about soft biometrics features in multiple scenarios
where the environment and visual cues are not known. This characteristic of human
gait has numerous application in the field of surveillance, advertisement and monitoring
and provides a practical approach in numerous real world applications such as motion
sensor, alarms and detections. Moreover, since human gait style varies from person
to person, the human gait is believed to be unique. To estimate a person’s age by
using non-visual features is a very effective task and can help us in various domains
like, soft-biometrics based applications, forensic medicine and forensic anthropology,
human computer interaction (HCI), surveillance (where visual cues are hidden), criminal
investigation, targeted campaigns and advertisement, security and safety management
systems etc. In this thesis, we propose a deep learning based model to analyze inertial
human gait data in order to estimate subject’s age. We used our previous gait data set
collected for 40 subjects using on-body inertial sensors. Each step and stride are collected
for 40 subjects (26 Males:14 Females) with accelerometer and gyro meter reading in the
3d (x,y,z) planes. We employ Long Short Term Memory (LSTM) networks to utilize
the memory of the networks for adjacent steps and strides of a subject to estimate
subject’s age. The results are generated by raw data vectors and normalized vectors for
the LSTM. We observe that normalized data is able to converge better with small batch
size. The classification results in 87% accuracy with different training windows and
batch sizes
