Emotion recognition via gait analysis is an active and key area of research because of its significant academic and commercial potential. With recent developments in hardware technology, the use of inertial sensors allows researchers to effectively capture the human motion data for gait analysis. To this end, the aim of this paper is to identify emotions from the inertial signals of human gait recorded by means of body mounted smartphones. We extracted a manually-crafted set of features computed from the human inertial gait data which are used to train and precisely predict the human emotions. Specifically, we collected the inertial gait data of 40 volunteers by means of smartphone’s on-board inertial measurement units (3D accelerometer, 3D gyroscope) attached at the chest in six basic emotions including sad, happy, anger, surprise, disgust and fear. Using stride based segmentation, the raw signals are first decomposed into individual strides. For each stride, a set of 296 spectro-temporal features are computed, which are fed into two supervised learning predictors namely Support Vector Machines and Random Forest. The classification results obtained with the proposed methodology and validated with k-fold validation procedure show classification accuracy of 95% for binary emotions and 86% for all six categories of emotions.


Workflow of the proposed emotion classification method


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How to cite:

MUHAMMAD ARSLAN HASHMI, QAISER RIAZ, MUHAMMAD ZEESHAN, MUHAMMAD SHAHZAD, and MUHAMMAD MOAZAM FRAZ, “Motion Reveal Emotions: Identifying Emotions from Human Walk Using Chest Mounted Smartphone”, IEEE Sensors Vol No 2020, DOI :