Editorial Sensing, Perceiving, and Understanding Actions

In this final part, I address the social nature of human thought through a discussion of the notion of the functional determination of thinking, the role of the 'recapitulation theory' in activity theory, and a polemic with David Bakhurst's ideas on the relation between the historical and the logical aspects of activity as an explanatory principle.

Ic Ai, 2008

This paper presents a distributed model for detecting Activities of Daily Living (ADLs) in a home setting. We consider an environment where household devices and utensils are augmented with simple sensors. The variation of object usage and shortage of training data makes it infeasible to construct high level models for ADLs in this environment. However, ADLs produce a series of sensor activations and the sensor activation sequences can help identifying the activity being performed. Hence, we try to correlate the sensor activation sequences with the activities. We provide a simple distributed linear time inference algorithm for this. The algorithm scales well with the size of the sensor network and number of activities to be detected.

ACM Transactions on Interactive Intelligent Systems, 2015

This editorial introduction describes the aims and scope of the ACM Transactions on Interactive Intelligent Systems special issue on Activity Recognition for Interaction. It explains why activity recognition is becoming crucial as part of the cycle of interaction between users and computing systems, and it shows how the five articles selected for this special issue reflect this theme.

International Journal of Advance Research in Computer Science and Management Studies [IJARCSMS] ijarcsms.com, 2020

Gotten from fast advances in computer vision and AI, video investigation errands have been moving from inferring the present state to anticipating the future state. Vision-based activity acknowledgment and forecast from recordings are such assignments, where activity acknowledgment is to human actions in light of complete activity executions, and activity expectation to foresee human actions in view of fragmented activity executions. These two undertakings have become especially predominant subjects as of late on account of their violently developing genuine applications, for example, visual reconnaissance, self-sufficient driving vehicle, amusement, and video recovery, and so forth. Numerous endeavors have been dedicated in the last a couple of decades so as to manufacture a strong and successful structure for activity acknowledgment and expectation. This paper, overview the total best in class systems in the activity acknowledgment andforecast.

IEEE transactions on cybernetics, 2013

Activity recognition has received increasing attention from the machine learning community. Of particular interest is the ability to recognize activities in real time from streaming data, but this presents a number of challenges not faced by traditional offline approaches. Among these challenges is handling the large amount of data that does not belong to a predefined class. In this paper, we describe a method by which activity discovery can be used to identify behavioral patterns in observational data. Discovering patterns in the data that does not belong to a predefined class aids in understanding this data and segmenting it into learnable classes. We demonstrate that activity discovery not only sheds light on behavioral patterns, but it can also boost the performance of recognition algorithms. We introduce this partnership between activity discovery and online activity recognition in the context of the CASAS smart home project and validate our approach using CASAS data sets.

Handbook of Theories of Social Psychology: Volume 1, 2012

The theory of action identification is a system of three principles explaining how people's thoughts of what they are doing relate to what they do. In a sense, the principles suggest an "operating system" for a human being -a program linking thought to action. The principles suggest simply that people do what they think they are doing, that when they can think of doing something more, they do that -but that when they can't do what they were thinking of doing, they think of doing less. Stated this way, the principles may seem perhaps too simple, but their interaction and ramifications are remarkably complex and predictive of a rich array of phenomena. The theory has things to say about how people can conceptualize their actions optimally, it offers insights into how people regulate actions through thinking, it provides a template for understanding how action connects to emotion and to self-concept, and it suggests how social influence occurs by changing how actions are understood. These consequences of the theory were discovered early on, but have recently been supplemented by extensions to encompass the dynamics of action and the role of action identification in the understanding of own and others' minds.

Identifying human activities is a key task for the development of advanced and effective ubiquitous applications in fields like Ambient Assisted Living. Depending on the availability of labeled data, recognition methods can be categorized as either supervised or unsupervised. Designing a comprehensive activity recognition system that works on a real-world setting is extremely challenging because of the difficulty for computers to process the complex nature of the human behaviors.

2020

Human Activity Recognition (HAR) has explored a lot recently in the academia and industries for numerous applications. There are lots of progress in the domain of vision-based action or activity recognition due to the advent of deep learning models and due to the availability of very large datasets in the last several years. However, there are still a number of genuine challenges in vision-based HAR. On the other hand, sensor-based HAR has more constraints to decipher, and is still far from maturity due to various challenges. These core challenging issues are addressed in this chapter. The challenges regarding data collection issues have been discussed in detail. Prospective research works and challenges in the field of sensor-based activity recognition have been discussed in terms of new researchers perspective, possible developments in industries for experts, and smart IoT solutions in the medical sector.

International journal of management, technology, and social science, 2024

The goal of this research paper is to provide a knowledge of the current state of the Human Activity Recognition (HAR) by carefully combining the available HAR literature. The essay also tries to provide a suitable HAR system that may be utilized for real-time activity detection, including healthcare, surveillance, and suspicious conduct. With a focus on complex and multi-task human activity recognition across various domains, this review study aims to examine the state of human activity detection and recognition techniques while also outlining promising directions for future research and development in the area. Design/Methodology/Approach: By gathering and evaluating the necessary material from worldwide and national journals, conferences, databases, and other resources found through Google Scholar and other search engines, a systematic literature review process was employed. Findings/Result: The comprehensive analysis of the study revealed several techniques for identifying and detecting human activity. There is still room to investigate the role of this technology in different domains to improve its robustness in detecting and recognizing of multiple human actions from preloaded CCTV cameras, which can aid in detecting abnormal and suspicious activities and ultimately reduce aberrant human actions in society. This is true even though the current study reports the investigations of several aspects of Human activity detection and recognition. Originality/Value: This essay proposes a concept map and uses a methodical approach to analyze the variables that affect the identification and detection of human activities. The research project adds to the growing body of study on information sharing by demonstrating its importance.