Human Activity Recognition and Pattern Discovery

By Eunju Kim, A. Helal, Diane Joyce Cook. IEEE pervasive computing, 2010

676 citations recorded by the source corpus.

In principle, activity recognition can be exploited to great societal benefits, especially in real-life, human centric applications such as elder care and healthcare. This article focused on recognizing simple human activities. Recognizing complex activities remains a challenging and active area of research and the nature of human activities poses different challenges. Human activity understanding encompasses activity recognition and activity pattern discovery. The first focuses on accurate detection of human activities based on a predefined activity model. An activity pattern discovery researcher builds a pervasive system first and then analyzes the sensor data to discover activity patterns.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/7b5288c665d80b7ea8291eb9b98f335b222a8815

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Control and Systems Engineering

Citations in the corpus

A Survey on Human Activity Recognition using Wearable Sensors (2013, background): “Some of these challenges are (1) the selection of the attributes to be measured, (2) the construction of a portable, unobtrusive, and inexpensive data acquisition system, (3) the design of feature extraction and inference methods, (4) the collection of data under realistic conditions, (5) the flexib”
Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors (2014, background): “Some of the issues they face are the following: obtrusiveness, occlusion, multiple people in the scene (camera-based system), aging (kinematics characteristics change over time), privacy, computational complexity, cost, noise, defining a threshold (threshold-based systems) [9–11].”
Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0 (2018, methodology): “A variety of machine learning algorithms have been used to process human activity data in the big data environment [15–20].”
Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation (2013, background): “Health-assistive smart homes are receiving increasing attention [80] and the need for activity recognition systems is great [81].”
Transfer Learning for Activity Recognition: A Survey (2013, background): “There exist a number of approaches to activity recognition [28] that vary depending on the underlying sensor technologies that are used to monitor activities, the alternative machine-learning algorithms that are used to model the activities and the realism of the testing environment.”
Activity Recognition on Streaming Sensor Data (2012, background): “It is a well researched area and hence there exists a number of approaches for activity recognition [11].”