Exploiting Passive RFID Technology for Activity Recognition in Smart Homes

Fortin-Simard, Dany; Bilodeau, Jean-Sébastien; Bouchard, Kévin; Gaboury, Sébastien; Bouchard, Bruno; Bouzouane, Abdenour

doi:10.1109/mis.2015.18

Cited by 89 publications

(37 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And video sensors are also affected by factors such as day and night, the environment, and so on. For wearable sensors, Kevin Bouchard et al use passive RFIDbased activity recognition systems to detect anomalies in cognitive impairment [7]. Yang et al proposed a simple method for identifying human activities based on simple object information involved in RFID usage activities [8].…”

Section: Related Workmentioning

confidence: 99%

Activity Feature Solving Based on TF‐IDF for Activity Recognition in Smart Homes

Guo

Xiong

et al. 2019

Complexity

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Activity Feature Solving Based on TF‐IDF for Activity Recognition in Smart Homes

Guo

Xiong

et al. 2019

Complexity

View full text Add to dashboard Cite

show abstract

“…These studies usually require high-performance devices or cameras (leading to privacy concerns) to make accurate calculations due to the requirements on the outdoor environments. Indoor examples for this task include using various videos/images such as from a monocular camera on top of a door [17], multiple cameras in smart environments [18,19], infrared and ultrasonic sensors [20,21], Wi-Fi signals [22][23][24][25], RFID [26], structural vibrational sensing [27], CO 2 sensors, and microphones [28]. All these methods might provide good results depending on the environment and fine-tuning, but sacrificing security/privacy of users (camera-based and Wi-Fi based solutions), having high computation overhead (camera-based solutions), or having low accuracy due to low data quality (ultrasonic-, infrared-, and RFID-based solutions, among others).…”

Section: Number Of People Estimationmentioning

confidence: 99%

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

Singh

Akşanlı

2019

JSAN

View full text Add to dashboard Cite

This paper presents a framework to accurately and non-intrusively detect the number of people in an environment and track their positions. Different from most of the previous studies, our system setup uses only ambient thermal sensors with low-resolution, using no multimedia resources or wearable sensors. This preserves user privacy in the environment, and requires no active participation by the users, causing no discomfort. We first develop multiple methods to estimate the number of people in the environment. Our experiments demonstrate that algorithm selection is very important, but with careful selection, we can obtain up to 100% accuracy when detecting user presence. In addition, we prove that sensor placement plays a crucial role in the system performance, where placing the sensor on the room ceiling yields to the best results. After accurately finding the number of people in the environment, we perform position tracking on the collected ambient data, which are thermal images of the space where there are multiple people. We consider position tracking as static activity detection, where the user's position does not change while performing activities, such as sitting, standing, etc. We perform efficient pre-processing on the data, including normalization and resizing, and then feed the data into well-known machine learning methods. We tested the efficiency of our framework (including the hardware and software setup) by detecting four static activities. Our results show that we can achieved up to 97.5% accuracy when detecting these static activities, with up to 100% class-wise precision and recall rates. Our framework can be very beneficial to several applications such as health-care, surveillance, and home automation, without causing any discomfort or privacy issues for the users.

show abstract

“…The book statistics function and book search function (Fortinsimard et al 2015) is mainly used to quickly count the number of books in the library and provide readers with accurate book location services. The basic principle of the two functions is the same, as shown in Figure 4.…”

Section: Intelligent Library Management Based On Rfidmentioning

confidence: 99%

Intelligent Electronic Management of Library by Radio Frequency Identification Technology

Qing-lan

Huang²

2019

Data Science Journal

View full text Add to dashboard Cite

In the information era, the knowledge learned in the campus can hardly meet the needs of the society, so the library has become one of the choices for people to supplement their knowledge. With the increasing demand for libraries, the traditional library management method has been difficult to support the demand. In order to promote the intelligent transformation of the library, the basic structure and principle of Radio Frequency Identification Devices (RFID) technology were briefly introduced in this paper, and the intelligent library management system based on RFID was also introduced. Then the library of Huaqiao University was divided into two regions, and the performance of collection counting, book query and passage efficiency of traditional library management based on magnetic stripe together with bar code and intelligent library management based on RFID were compared. The results showed that the collection counting efficiency of intelligent libraries was much higher than that of traditional libraries, the high accuracy and stability of book query avoided the dislocation and chaos like traditional libraries, and there was no congestion even during rush hours because of the higher passing efficiency.

show abstract

Exploiting Passive RFID Technology for Activity Recognition in Smart Homes

Cited by 89 publications

References 5 publications

Activity Feature Solving Based on TF‐IDF for Activity Recognition in Smart Homes

Activity Feature Solving Based on TF‐IDF for Activity Recognition in Smart Homes

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

Intelligent Electronic Management of Library by Radio Frequency Identification Technology

Contact Info

Product

Resources

About