Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes
Smart homes based on the Internet of Things have been rapidly developed. To improve the safety, comfort, and convenience of residents’ lives with minimal cost, daily activity recognition aims to know resident’s daily activity in non-invasive manner. The performance of daily activity recognition heav...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/5245373 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850185724348858368 |
|---|---|
| author | Jinghuan Guo Yong Mu Mudi Xiong Yaqing Liu Jingxuan Gu |
| author_facet | Jinghuan Guo Yong Mu Mudi Xiong Yaqing Liu Jingxuan Gu |
| author_sort | Jinghuan Guo |
| collection | DOAJ |
| description | Smart homes based on the Internet of Things have been rapidly developed. To improve the safety, comfort, and convenience of residents’ lives with minimal cost, daily activity recognition aims to know resident’s daily activity in non-invasive manner. The performance of daily activity recognition heavily depends on solving strategy of activity feature. However, the current common employed solving strategy based on statistical information of individual activity does not support well the activity recognition. To improve the common employed solving strategy, an activity feature solving strategy based on TF-IDF is proposed in this paper. The proposed strategy exploits statistical information related to both individual activity and the whole of activities. Two distinct datasets have been commissioned, to mitigate against any possible effect of coupling between dataset and sensor configuration. Finally, a number of machine learning (ML) techniques and deep learning technique have been evaluated to assess their performance for residents activity recognition. |
| format | Article |
| id | doaj-art-41acdcf8e0a04f26bc96e4dec0b76b67 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-41acdcf8e0a04f26bc96e4dec0b76b672025-08-20T02:16:39ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/52453735245373Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart HomesJinghuan Guo0Yong Mu1Mudi Xiong2Yaqing Liu3Jingxuan Gu4School of Information Science & Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of Information Science & Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of Information Science & Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of Information Science & Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of Mathematical Sciences, Dalian University of Technology, Dalian 116026, ChinaSmart homes based on the Internet of Things have been rapidly developed. To improve the safety, comfort, and convenience of residents’ lives with minimal cost, daily activity recognition aims to know resident’s daily activity in non-invasive manner. The performance of daily activity recognition heavily depends on solving strategy of activity feature. However, the current common employed solving strategy based on statistical information of individual activity does not support well the activity recognition. To improve the common employed solving strategy, an activity feature solving strategy based on TF-IDF is proposed in this paper. The proposed strategy exploits statistical information related to both individual activity and the whole of activities. Two distinct datasets have been commissioned, to mitigate against any possible effect of coupling between dataset and sensor configuration. Finally, a number of machine learning (ML) techniques and deep learning technique have been evaluated to assess their performance for residents activity recognition.http://dx.doi.org/10.1155/2019/5245373 |
| spellingShingle | Jinghuan Guo Yong Mu Mudi Xiong Yaqing Liu Jingxuan Gu Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes Complexity |
| title | Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes |
| title_full | Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes |
| title_fullStr | Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes |
| title_full_unstemmed | Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes |
| title_short | Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes |
| title_sort | activity feature solving based on tf idf for activity recognition in smart homes |
| url | http://dx.doi.org/10.1155/2019/5245373 |
| work_keys_str_mv | AT jinghuanguo activityfeaturesolvingbasedontfidfforactivityrecognitioninsmarthomes AT yongmu activityfeaturesolvingbasedontfidfforactivityrecognitioninsmarthomes AT mudixiong activityfeaturesolvingbasedontfidfforactivityrecognitioninsmarthomes AT yaqingliu activityfeaturesolvingbasedontfidfforactivityrecognitioninsmarthomes AT jingxuangu activityfeaturesolvingbasedontfidfforactivityrecognitioninsmarthomes |