Data Collection Module for Human Activity Recognition
Dejan Gjorgjevikj and Gjorgji Madjarov
Abstract: Unobtrusive human activity monitoring using cheap and widely available sensors are the future for human activity recognition. It will support the extensive penetration of new applications in Ambient Assisted Living (AAL), Smart Homes (SH), Smart Cities (SC) and Health Monitoring (HM). The biggest challenges in these applications are the automatic processing and analyzing the large amounts of sensory data as well as building machine learning models for monitoring, detection, recognition and prediction of an activity, movement, state or an event. The aim of this research is to develop a data collection system that will enable detection and monitoring of human activity using very low-cost, unobtrusive passive infrared and microwave radar sensors. Our data collection module is composed of Arduino microcontroller, SD card module and real time clock module and enables connecting several sensors which measurements are to be logged. In our experiments we used a modified microwave radar sensor RCWL-0516 and a modified passive infrared sensor HC-SR501. Both are extremely low cost, easily accessible sensors usually used for general purpose applications like motion detection for light switching. Both sensors were modified in a way to make the raw analog output of the sensor available for logging by the microcontroller. The data collection module enables collecting measurements of up to 4 analog (10-bit precision), and up to 8 digital sensor inputs with sampling rates of up to 200 samples per second. The measurements are logged on a SD card including a precise timestamp that will enable the logs of several modules to be joined together keeping the time alignment of the readings. A separate setup for synchronized initialization of the RTC modules of the separate sensor modules is also presented. A series of experiments in a control environment with volunteers were conducted and the collected data from the sensors are pre-processed and labelled for further analysis and application of machine learning based approaches for automatic recognition and monitoring of human activity.