Exploring LoRa for Long-range Through-wall Sensing

Zhang, Fusang; Chang, Zhaoxin; Niu, Kai; Xiong, Jie; Jin, Beihong; Lv, Qin; Zhang, Daqing

doi:10.1145/3397326

Cited by 70 publications

(28 citation statements)

References 45 publications

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“…But the commodity devices for BLE, LoRa cannot provide rich channel information, which results in relatively lower localization accuracy than those based on WIFI [26][27][28][29][31][32][33]. To improve their accuracy, some attempts have been made to build the prototype based on SDR devices [30,35,52]. Those work process the raw signal data and achieve similar or higher precision than WIFI-based systems.…”

Section: Related Workmentioning

confidence: 99%

TyrLoc

Yin

et al. 2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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This work presents the design and implementation of TyrLoc, an accurate multi-technology switching MIMO localization system that can be deployed on low-cost SDRs. TyrLoc only uses a single RF Chain to switch on each antenna in an antenna array within the coherence time asynchronously, thus mimicking a MIMO platform to pinpoint the positions of WIFI, Bluetooth Low Energy (BLE) and LoRa devices. TyrLoc makes three key technical contributions. First, TyrLoc modifies the firmware of inexpensive PlutoSDR that controls the antenna switching pattern and tags the signal associated with each antenna. Second, it develops a two-stage fine-grained carrier frequency offset (CFO) calibration algorithm that harnesses the agile antenna switching pattern and is 10× more accurate than the baseline method. Third, TyrLoc employs an interpolated transform approach to facilitate angle-of-arrival (AoA) estimation in the presence of missing antennas. The AoA-based localization experiments in a multipath-rich indoor environment show that TyrLoc with eight antennas achieves the median errors of 63cm for WIFI, 39cm for BLE and 32cm for LoRa, respectively. CCS CONCEPTS• Networks → Location based services.

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Section: Related Workmentioning

confidence: 99%

TyrLoc

Yin

et al. 2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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“…e image-based research method can be more intuitive and accurate and can better identify the complex motion state. At present, the research on human activity recognition in China is mainly based on image analysis [9,10]. is method has its advantages, but its disadvantages are also obvious.…”

Section: Introductionmentioning

confidence: 99%

Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning

Cao

Lin

Wang

et al. 2021

Mobile Information Systems

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The field of activity recognition has evolved relatively early and has attracted countless researchers. With the continuous development of science and technology, people’s research on human activity recognition is also deepening and becoming richer. Nowadays, whether it is medicine, education, sports, or smart home, various fields have developed a strong interest in activity recognition, and a series of research results have also been put into people’s real production and life. Nowadays, smart phones have become quite popular, and the technology is becoming more and more mature, and various sensors have emerged at the historic moment, so the related research on activity recognition based on mobile phone sensors has its necessity and possibility. This article will use an Android smartphone to collect the data of six basic behaviors of human, which are walking, running, standing, sitting, going upstairs, and going downstairs, through its acceleration sensor, and use the classic model of deep learning CNN (convolutional neural network) to fuse those multidimensional mobile data, using TensorFlow for model training and test evaluation. The generated model is finally transplanted to an Android phone to complete the mobile-end activity recognition system.

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“…By analyzing the variations of signal reflected from the target, rich target information can be obtained such as the movement speed and displacement. With the latest advance of wireless sensing, researches have successfully exploited WiFi [42], RFID [10], LoRa [41], acoustic [34] and 60 GHz [28] signals to accurately monitor the finegrained respiration. However, one interesting observation is that while there are a lot of studies on respiration monitoring already, there is very little work [26] on heartbeat monitoring.…”

Section: Introductionmentioning

confidence: 99%

Your Smart Speaker Can "Hear" Your Heartbeat!

Zhang

Wang

Jin

et al. 2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

Self Cite

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Vital sign monitoring is a common practice amongst medical professionals, and plays a key role in patient care and clinical diagnosis. Traditionally, dedicated equipment is employed to monitor these vital signs. For example, electrocardiograms (ECG) with 3-12 electrodes are attached to the target chest for heartbeat monitoring. In the last few years, wireless sensing becomes a hot research topic and wireless signal itself is utilized for sensing purposes without requiring the target to wear any sensors. The contact-free nature of wireless sensing makes it particularly appealing in current COVID-19 pandemic. Recently, promising progress has been achieved and the sensing granularity has been pushed to millimeter level, fine enough to monitor respiration which causes a chest displacement of 5 mm. While a great success with respiration monitoring, it is still very challenging to monitor heartbeat due to the extremely subtle chest displacement (0.1 - 0.5 mm) - smaller than 10% of that caused by respiration. What makes it worse is that the tiny heartbeat-caused chest displacement is buried inside the respiration-caused displacement. In this paper, we show the feasibility of employing the popular smart speakers (e.g., Amazon Echo) to monitor an individual's heartbeats in a contact-free manner. To extract the submillimeter heartbeat motion in the presence of other interference movements, a series of novel signal processing schemes are employed. We successfully prototype the first real-time heartbeat monitoring system using a commodity smart speaker. Experiment results show that the proposed system can monitor a target's heartbeat accurately, achieving a median heart rate estimation error of 0.75 beat per minute (bpm), and a median heartbeat interval estimation error of 13.28 ms (less than 1.8%), outperforming even some popular commodity products available on the market.

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Exploring LoRa for Long-range Through-wall Sensing

Cited by 70 publications

References 45 publications

TyrLoc

TyrLoc

Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning

Your Smart Speaker Can "Hear" Your Heartbeat!

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