Deep Learning for RFID-Based Activity Recognition

Li, Xinyu; Zhang, Yanyi; Marsic, Ivan; Sarcevic, Aleksandra; Burd, Randall S.

doi:10.1145/2994551.2994569

Cited by 123 publications

(64 citation statements)

References 26 publications

(33 reference statements)

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“…For instance, the accelerometer attached to a cup can be used to detect the drinking water activity. Radio frequency identifier (RFID) tags are typically used as object sensors and deployed in smart home environment (Vepakomma et al, 2015;Yang et al, 2015;Fang and Hu, 2014) and medical activities (Li et al, 2016b;Wang et al, 2016a). The RFID can provide more fine-grained information for more complex activity recognition.…”

Section: Object Sensormentioning

confidence: 99%

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Deep learning for sensor-based activity recognition: A survey

Wang

Chen

Hao

et al. 2019

Pattern Recognition Letters

1,503

927

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Research Highlights (Required)To create your highlights, please type the highlights against each \item command.It should be short collection of bullet points that convey the core findings of the article. It should include 3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point.)• We survey deep learning based HAR in sensor modality, deep model, and application.• We comprehensively discuss the insights of deep learning models for HAR tasks.• We extensively investigate why deep learning can improve the performance of HAR.• We also summarize the public HAR datasets frequently used for research purpose.• We present some grand challenges and feasible solutions for deep learning based HAR. ABSTRACTSensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.

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Section: Object Sensormentioning

confidence: 99%

“…In (Singh et al, 2017), pressure sensor data was transformed to the image via modality transformation. Other similar work include (Ravi et al, 2016;Li et al, 2016b). This model-driven approach can make use of the temporal correlation of sensor.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Deep learning for sensor-based activity recognition: A survey

Wang

Chen

Hao

et al. 2019

Pattern Recognition Letters

1,503

927

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“…• We share the finding that deep convolutional neural network (CNN) architectures can automatically learn discriminative features from our unique ID-Sensor data with embedded low resolution acceleration information in an RFID-only data stream. To the best of our knowledge, only one other study has considered the problem of human activity recognition from raw RFID-only data streams using a deep learning paradigm [27]. • We demonstrate, for the first time and to the best of our knowledge, the capability to employ a low cost bodyworn passive UHF RFID tag for sensing of hospitalised patient activities.…”

Section: A Contributionsmentioning

confidence: 86%

Super Low Resolution RF Powered Accelerometers for Alerting on Hospitalized Patient Bed Exits

Chesser

Jayatilaka

Visvanathan

et al. 2019

2019 IEEE International Conference on Pervasive Computing and Communications (PerCom

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Falls have serious consequences and are prevalent in acute hospitals and nursing homes caring for older people. Most falls occur in bedrooms and near the bed. Technological interventions to mitigate the risk of falling aim to automatically monitor bed-exit events and subsequently alert healthcare personnel to provide timely supervisions. We observe that frequency-domain information related to patient activities exist predominantly in very low frequencies. Therefore, we recognise the potential to employ a low resolution acceleration sensing modality in contrast to powering and sensing with a conventional MEMS (Micro Electro Mechanical System) accelerometer. Consequently, we investigate a batteryless sensing modality with low cost wirelessly powered Radio Frequency Identification (RFID) technology with the potential for convenient integration into clothing, such as hospital gowns. We design and build a passive accelerometerbased RFID sensor embodiment-ID-Sensor-for our study. The sensor design allows deriving ultra low resolution acceleration data from the rate of change of unique RFID tag identifiers in accordance with the movement of a patient's upper body. We investigate two convolutional neural network architectures for learning from raw RFID-only data streams and compare performance with a traditional shallow classifier with engineered features. We evaluate performance with 23 hospitalized older patients. We demonstrate, for the first time and to the best of knowledge, that: i) the low resolution acceleration data embedded in the RF powered ID-Sensor data stream can provide a practicable method for activity recognition; and ii) highly discriminative features can be efficiently learned from the raw RFID-only data stream using a fully convolutional network architecture.

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“…But their presence can be decided. As the main function of pooling is to reduce the amount of parameters and computation time, as well as increase the CNN's robustness to data translations, the pooling layers can be omitted for input images with relatively small dimensions [27]. Our tests show that, by omitting the pooling lays, the accuracy of the CNN increases from 99.7% to 99.9%, probably due to the small dimensions of the spectrogram.…”

Section: Presence Of Pooling Layersmentioning

confidence: 92%

Deep Room Recognition Using Inaudible Echos

Song

Tan

2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Recent years have seen the increasing need of location awareness by mobile applications. This paper presents a room-level indoor localization approach based on the measured room's echos in response to a two-millisecond single-tone inaudible chirp emitted by a smartphone's loudspeaker. Different from other acoustics-based room recognition systems that record full-spectrum audio for up to ten seconds, our approach records audio in a narrow inaudible band for 0.1 seconds only to preserve the user's privacy. However, the short-time and narrowband audio signal carries limited information about the room's characteristics, presenting challenges to accurate room recognition. This paper applies deep learning to effectively capture the subtle fingerprints in the rooms' acoustic responses. Our extensive experiments show that a two-layer convolutional neural network fed with the spectrogram of the inaudible echos achieve the best performance, compared with alternative designs using other raw data formats and deep models. Based on this result, we design a RoomRecognize cloud service and its mobile client library that enable the mobile application developers to readily implement the room recognition functionality without resorting to any existing infrastructures and add-on hardware. Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and 89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in a quiet museum, and 15 spots in a crowded museum, respectively. Compared with the state-of-the-art approaches based on support vector machine, RoomRecognize significantly improves the Pareto frontier of recognition accuracy versus robustness against interfering sounds (e.g., ambient music).

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Deep Learning for RFID-Based Activity Recognition

Cited by 123 publications

References 26 publications

Deep learning for sensor-based activity recognition: A survey

Deep learning for sensor-based activity recognition: A survey

Super Low Resolution RF Powered Accelerometers for Alerting on Hospitalized Patient Bed Exits

Deep Room Recognition Using Inaudible Echos

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