Multimodal approach for non-tagged indoor identification and tracking using smart floor and pyroelectric infrared sensors

Ibrahim, Abdelhameed; Wong, Chi Biu; Moore, Philip; Chen, Xi

doi:10.1504/ijcse.2017.081172

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…However, the existing monitoring devices restrict movement since they require tracking devices to be worn on the body or always carried. Therefore, a smart floor monitoring system, which obtains sensory information from human activities such as walking, position detection, and activity status, has gained considerable attention for the home training and health monitoring of patients at home or in hospitals without wearing or carrying devices [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Smart Floor Mats for a Health Monitoring System Based on Textile Pressure Sensing: Development and Usability Study

Kim¹,

Jang²,

Lee³

et al. 2023

JMIR Form Res

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Background The rise in single-person households has resulted in social problems like loneliness and isolation, commonly known as “death by loneliness.” Various factors contribute to this increase, including a desire for independent living and communication challenges within families due to societal changes. Older individuals living alone are particularly susceptible to loneliness and isolation due to limited family communication and a lack of social activities. Addressing these issues is crucial, and proactive solutions are needed. It is important to explore diverse measures to tackle the challenges of single-person households and prevent deaths due to loneliness in our society. Objective Non–face-to-face health care service systems have gained widespread interest owing to the rapid development of smart home technology. Particularly, a health monitoring system must be developed to manage patients’ health status and send alerts for dangerous situations based on their activity. Therefore, in this study, we present a novel health monitoring system based on the auto-mapping method, which uses real-time position sensing mats. Methods The smart floor mats are operated as piezo-resistive devices, which are composed of a carbon nanotube–based conductive textile, electrodes, main processor circuit, and a mat. The developed smart floor system acquires real-time position information using a multiconnection method between the modules based on the auto-mapping algorithm, which automatically creates a spatial map. The auto-mapping algorithm allows the user to freely set various activity areas through floor mapping. Then, the monitoring system was evaluated in a room with an area of 41.3 m2, which is embedded with the manufactured floor mats and monitoring application. Results This monitoring system automatically acquires information on the total number, location, and direction of the mats and creates a spatial map. The position sensing mats can be easily configured with a simple structure by using a carbon nanotube–based piezo-resistive textile. The mats detect the activity in real time and record location information since they are connected through auto-mapping technology. Conclusions This system allows for the analysis of patients’ behavior patterns and the management of health care on the web by providing important basic information for activity patterns in the monitoring system. The proposed smart floor system can serve as the foundation for smart home applications in the future, which include health care, intelligent automation, and home security, owing to its advantages of low cost, large area, and high reliability.

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Section: Introductionmentioning

confidence: 99%

Smart Floor Mats for a Health Monitoring System Based on Textile Pressure Sensing: Development and Usability Study

Kim¹,

Jang²,

Lee³

et al. 2023

JMIR Form Res

View full text Add to dashboard Cite

show abstract

“…Among these, gait recognition is an emerging biometric technology that offers several advantages over fingerprint and face recognition, including non-invasiveness, long-range identification capability, minimal environmental influence, and difficulty in disguising. Traditional gait recognition research often involves the use of cameras [8,9], acceleration sensors [10], and ground sensors [11] to capture movement information. However, these devices are susceptible to issues such as illumination variations, smoke interference, and physical obstacles, which significantly limit their effectiveness in harsh mining environments.…”

Section: Introductionmentioning

confidence: 99%

Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa

et al. 2023

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This study proposes a new approach to gait recognition using LoRa signals, taking into account the challenging conditions found in underground coal mines, such as low illumination, high temperature and humidity, high dust concentrations, and limited space. The aim is to address the limitations of existing gait recognition research, which relies on sensors or other wireless signals that are sensitive to environmental factors, costly to deploy, invasive, and require close sensing distances. The proposed method analyzes the received signal waveform and utilizes the amplitude data for gait recognition. To ensure data reliability, outlier removal and signal smoothing are performed using Hampel and S-G filters, respectively. Additionally, high-frequency noise is eliminated through the application of Butterworth filters. To enhance the discriminative power of gait features, the pre-processed data are reconstructed using an autoencoder, which effectively extracts the underlying gait behavior. The trained autoencoder generates encoder features that serve as the input matrix. The Softmax method is then employed to associate these features with individual identities, enabling LoRa-based single-target gait recognition. Experimental results demonstrate significant performance improvements. In indoor environments, the recognition accuracy for groups of 2 to 8 individuals ranges from 99.7% to 96.6%. Notably, in an underground coal mine where the target is located 20 m away from the transceiver, the recognition accuracy for eight individuals reaches 93.3%.

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Human Identification for Activities of Daily Living: A Deep Transfer Learning Approach

Zhu

Samtani

Chen

et al. 2020

Journal of Management Information Systems

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Multimodal approach for non-tagged indoor identification and tracking using smart floor and pyroelectric infrared sensors

Cited by 6 publications

References 23 publications

Smart Floor Mats for a Health Monitoring System Based on Textile Pressure Sensing: Development and Usability Study

Smart Floor Mats for a Health Monitoring System Based on Textile Pressure Sensing: Development and Usability Study

Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa

Human Identification for Activities of Daily Living: A Deep Transfer Learning Approach

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