Elderly people activity monitoring with involved binary sensors and Deep Convolution Neural Network

Rajesh, P.; Kavitha, R.

doi:10.1007/s00521-022-07268-4

Cited by 6 publications

(1 citation statement)

References 18 publications

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“…The DCNN model demonstrated high accuracy, significantly outperforming classical machine-learning classifiers in accurately detecting and interpreting elderly travel patterns. In a study by Rajesh et al [ 102 ], DCNN algorithms were also employed to detect a broader range of activities, specifically 12 activities. The system underwent evaluation using the Aruba dataset, and the results for detecting these 12 activities showcased a high F1 score of 0.82.…”

Section: Resultsmentioning

confidence: 99%

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

Shokrollahi,

Persson,

Malekian

et al. 2024

Sensors

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Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings’ status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

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Section: Resultsmentioning

confidence: 99%