Development of Cloud-based Infrastructure for Real Time Analysis of Wearable Sensor Signal

Abstract

This paper focuses on development of server-based infrastructure for real-time analysis of wearable signals. In this work, we have implemented a python flask-based API (Application Programming Interface) to receive sensor and image data from various platforms (e.g., mobile, computer), and created a data storage (MariaDB database and file server) to store data. A load balancer, Nginx, that redirects traffic into different ports was configured for low latency. Additionally, we developed a food intake detection method based on machine learning (ML). We have investigated ten different ML models to find an accurate and fast model. To test the server infrastructure, we conducted a functionality test to verify each component of the server. We also investigated how a number of APIs influence the performance of the server in terms of latency. To verify the server, we performed a computer simulation where a python script was used to deliver signals and images continuously to the server. We sent a total of five hundred images and sensor signals to the server from two different processes simultaneously. We achieved an average latency of 260ms and 110ms for signal and image packets, respectively. The average latency decreased by 26.92% and 15.38% when we use two API ports. For food intake detections, data were collected from 17 free-living (9 males, 6 females, and 2 adolescents) volunteers. Thereafter these data were evaluated by ten different ML classifiers, e.g., Adaboost (AB), Random Forest (RF), Gradient Boosting (GB) and Histogram Gradient Boosting (HGB). The experiments were performed by 5-fold validations, where 80% of subjects were used for training the remaining 20% for testing. The RF model provided the best result with average accuracy, precision, recall and F1-score of 0.99, 0.97, 0.97 and 0.98, respectively. Results indicate that our implemented server architecture was able to receive signals in real-time and detect food intake with high accuracy.