Implementation and Evaluation of Analog-PIR-Sensor-Based Activity Recognition

Abstract

: These days, smart home applications such as concierge service for residents, home appliance control, and so on are attracting attention. To realize these applications, we need a system which recognizes various human activities accurately with a low cost device. There are many studies on the activity recognition in a smart home. We also have proposed an activity recognition technique in a smart home by utilizing digital-output-PIR (passive infrared) sensors, door sensors, and power meters. However, the study has an unsolved issue: we cannot distinguish similar activities happening at the same place, for example, "eating" and "reading" while sitting on a sofa. In order to cope with this challenge, we introduce ALPAS: analog-output-PIR-sensor-based activity recognition technique which recognizes the different activities in the same place. Our technique recognizes user's activity by utilizing machine learning with frequency components of the sensor's output as features. However, because the number of features used in ALPAS is 1000 for each analog PIR-sensor, a large capacity memory is required. To reduce the number of features, we select a part of the sensing data. We call the starting point of the selected data as starting frequency (SF) and ending part as ending frequency (EF). We searched SF and EF using a grid search, and evaluated the recognition accuracy. We evaluated the proposed technique in a smarthome testbed. In the evaluation, five participants performed four different activities while sitting on a sofa. As a result, we achieved F-Measure: 63.9% when the EF is 1.4 Hz, and F-Measure: 50% or lower when the SF is 9.9 Hz or higher.

Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches

“…On the other hand, signal-based PIR sensors provide a more complicated output, often in the form of an analog signal that fluctuates with the intensity of observed infrared radiation [ 43 ]. This signal may give more precise information [ 48 ], such as the size or speed of the moving item, making these sensors suited for more advanced applications such as indoor localization or thorough motion analysis. Table 4 compares binary and signal-based PIR sensors in various aspects based on previous research.…”

“…This innovative approach effectively combines spatial and temporal data for simultaneous tracking and activity analysis. In another analog-based activity detection research by Fujiwara et al [ 48 ], an innovative activity recognition technique using an analog-output PIR sensor is developed, capable of identifying various activities at the same location. This technique leverages random forest machine learning, utilizing the frequency components of the sensor’s output as features.…”

PIR Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches

“…On the other hand, signal-based PIR sensors provide a more complicated output, often in the form of an analog signal that fluctuates with the intensity of observed infrared radiation [43]. This signal may give more precise information [48], such as the size or speed of the moving item, making these sensors suited for more advanced applications such as indoor localization or thorough motion analysis. Table 4 compares binary and signalbased PIR sensors in various aspects based on previous research.…”