ArticleSensors (Basel, Switzerland)2020
Habit Representation Based on Activity Recognition.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns.Sensors (Basel, Switzerland) · 2022Article
- Are Smart Homes Adequate for Older Adults with Dementia?Sensors (Basel, Switzerland) · 2022Review
- Multi-sensor dataset of human activities in a smart home environment.Data in brief · 2021Article
- Finding Characteristics of Users in Sensory Information: From Activities to Personality Traits.Sensors (Basel, Switzerland) · 2020Article
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2 authors.
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Abstract
With the increasing elderly population, attention has been drawn to the development of applications for habit assessment using activity data from smart environments that can be implemented in care facilities. In this paper, we introduce a novel habit assessment method based on information of human activities. First, a recognition system tracks the user's activities of daily living by collecting data from multiple object sensors and ambient sensors that are distributed within the environment. Based on this information, the activities of daily living are expressed using Fourier series representation. The durations and sequence of the activities are represented by the phases and amplitudes of the harmonics. In this manner, each sequence is represented in a form that we refer to as a behavioral spectrum. After that, signals are clustered to find habits. We also calculate the variability, and by comparing the explained variance, the types of habits are found. For an evaluation, two datasets (young and elderly population) were used, and the results showed the potential habits of each group. The outcomes of this study can help improve and expand the applications of smart homes.
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