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Body Sensor

The Experts below are selected from a list of 9348 Experts worldwide ranked by ideXlab platform

Jindong Tan – 1st expert on this subject based on the ideXlab platform

  • Body Sensor network based context-aware QRS detection
    Journal of Signal Processing Systems, 2012
    Co-Authors: Hongxing Wei, Huaming Li, Jindong Tan

    Abstract:

    In this paper, a Body Sensor network (BSN) based context-aware QRS detection scheme is proposed. The algorithm uses the context information provided by the Body Sensor network to improve the QRS detection performance by dynamically selecting those leads with the best SNR and taking advantage of the best features of two complementary detection algorithms. The accelerometer data from the BSN are used to classify the daily activities of patients and provide context information. The classification results indicate the types of activities that were engaged in. They also indicate their corresponding intensity, which is related to the signal-to-noise ratio (SNR) of the ECG recordings. Activity intensity is first fed to the lead selector to eliminate those leads with low SNR, and then is fed to a selector to select a proper QRS detector according to the noise level. An MIT-BIH noise stress test database is used to evaluate the algorithms.

  • ECG segmentation in a Body Sensor network using Hidden Markov Models
    2008 5th International Summer School and Symposium on Medical Devices and Biosensors, 2008
    Co-Authors: Huaming Li, Jindong Tan

    Abstract:

    A novel approach for segmenting ECG signal in a Body Sensor network employing hidden Markov modeling (HMM) technique is presented. In traditional HMM methods inadequate and slow parameter adaptation is largely responsible for the low positive predictivity rate. To solve the problem, we introduce an active HMM parameter adaptation and ECG segmentation algorithm. Body Sensor networks are used to pre-segment the raw ECG data by performing QRS detection. Instead of one single generic HMM, multiple individualized HMMs are used. Each HMM is only responsible for extracting the characteristic waveforms of the ECG signals with similar temporal features from the same group, so that the temporal parameter adaptation can be naturally achieved.

  • Body Sensor network based ECG segmentation and analysis.
    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2007
    Co-Authors: Huaming Li, Jindong Tan

    Abstract:

    In this paper, a Body Sensor network based ECG signal segmentation approach is presented. Hidden Markov Modeling (HMM) technique is employed. Since people’s heart rates vary a lot, the corresponding characteristic waveform intervals and durations change with time as well. For patients with cardiac diseases, such as arrhythmia, the heart beat interval may even change abruptly and irregularly. Because traditional HMM parameter adaptation is conservative and slow to respond to beat interval changes, inadequate and slow parameter adaptation is largely responsible for the low positive predictivity rate (+P). To solve the problem, we introduce an active HMM parameter adaptation and ECG segmentation algorithm, which includes three parts: the pre-segmentation and classification, the HMM model training, and the detailed segmentation. Body Sensor networks are used to collect and pre-segment the raw ECG data by performing QRS detection. Then the R-R interval information that directly reflects the beat variation is extracted and used to classify the raw ECG data into several groups. One specific HMM is trained for each of the groups. Instead of one single generic HMM, multiple individualized HMMs are set up and each HMM is only responsible for extracting the characteristic waveforms of the ECG signals with similar temporal features in the detailed segmentation, so that the temporal parameter adaptation can be naturally achieved.

Jian Lu – 2nd expert on this subject based on the ideXlab platform

  • a hierarchical approach to real time activity recognition in Body Sensor networks
    Pervasive and Mobile Computing, 2012
    Co-Authors: Liang Wang, Tao Gu, Jian Lu

    Abstract:

    Real-time activity recognition in Body Sensor networks is an important and challenging task. In this paper, we propose a real-time, hierarchical model to recognize both simple gestures and complex activities using a wireless Body Sensor network. In this model, we first use a fast and lightweight algorithm to detect gestures at the Sensor node level, and then propose a pattern based real-time algorithm to recognize complex, high-level activities at the portable device level. We evaluate our algorithms over a real-world dataset. The results show that the proposed system not only achieves good performance (an average utility of 0.81, an average accuracy of 82.87%, and an average real-time delay of 5.7 seconds), but also significantly reduces the network’s communication cost by 60.2%.

  • Adaptive and Radio-Agnostic QoS for Body Sensor Networks
    ACM Transactions in Embedded Computing Systems, 2011
    Co-Authors: Gang Zhou, Qiang Li, Jingyuan Li, Yafeng Wu, Jian Lu, Mark D. Yarvis, John A. Stankovic

    Abstract:

    As wireless devices and Sensors are increasingly deployed on people, researchers have begun to focus on wireless Body-area networks. Applications of wireless Body Sensor networks include healthcare, entertainment, and personal assistance, in which Sensors collect physiological and activity data from people and their environments. In these Body Sensor networks, quality of service is needed to provide reliable data communication over prioritized data streams. This article proposes BodyQoS, the first running QoS system demonstrated on an emulated Body Sensor network. BodyQoS adopts an asymmetric architecture, in which most processing is done on a resource-rich aggregator, minimizing the load on resource-limited Sensor nodes. A virtual MAC is developed in BodyQoS to make it radio-agnostic, allowing a BodyQoS to schedule wireless resources without knowing the implementation details of the underlying MAC protocols. Another unique property of BodyQoS is its ability to provide adaptive resource scheduling. When the effective bandwidth of the channel degrades due to RF interference or Body fading effect, BodyQoS adaptively schedules remaining bandwidth to meet QoS requirements. We have implemented BodyQoS in NesC on top of TinyOS, and evaluated its performance on MicaZ devices. Our system performance study shows that BodyQoS delivers significantly improved performance over conventional solutions in combating channel impairment.

  • Bodyqos adaptive and radio agnostic qos for Body Sensor networks
    International Conference on Computer Communications, 2008
    Co-Authors: Gang Zhou, Jian Lu, Mark D. Yarvis, John A. Stankovic

    Abstract:

    As wireless devices and Sensors are increasingly deployed on people, researchers have begun to focus on wireless Body-area networks. Applications of wireless Body Sensor networks include healthcare, entertainment, and personal assistance, in which Sensors collect physiological and activity data from people and their environments. In these Body Sensor networks, quality of service is needed to provide reliable data communication over prioritized data streams. This paper proposes BodyQoS, the first running QoS system demonstrated on an emulated Body Sensor network. BodyQoS adopts an asymmetric architecture, in which most processing is done on a resource rich aggregator, minimizing the load on resource limited Sensor nodes. A virtual MAC is developed in BodyQoS to make it radio-agnostic, allowing a BodyQoS to schedule wireless resources without knowing the implementation details of the underlying MAC protocols. Another unique property of BodyQoS is its ability to provide adaptive resource scheduling. When the effective bandwidth of the channel degrades due to RF interference or Body fading effect, BodyQoS adaptively schedules remaining bandwidth to meet QoS requirements. We have implemented BodyQoS in NesC on top of TinyOS, and evaluated its performance on MicaZ devices. Our system performance study shows that BodyQoS delivers significantly improved performance over conventional solutions in combating channel impairment.

Huaming Li – 3rd expert on this subject based on the ideXlab platform

  • Body Sensor network based context-aware QRS detection
    Journal of Signal Processing Systems, 2012
    Co-Authors: Hongxing Wei, Huaming Li, Jindong Tan

    Abstract:

    In this paper, a Body Sensor network (BSN) based context-aware QRS detection scheme is proposed. The algorithm uses the context information provided by the Body Sensor network to improve the QRS detection performance by dynamically selecting those leads with the best SNR and taking advantage of the best features of two complementary detection algorithms. The accelerometer data from the BSN are used to classify the daily activities of patients and provide context information. The classification results indicate the types of activities that were engaged in. They also indicate their corresponding intensity, which is related to the signal-to-noise ratio (SNR) of the ECG recordings. Activity intensity is first fed to the lead selector to eliminate those leads with low SNR, and then is fed to a selector to select a proper QRS detector according to the noise level. An MIT-BIH noise stress test database is used to evaluate the algorithms.

  • ECG segmentation in a Body Sensor network using Hidden Markov Models
    2008 5th International Summer School and Symposium on Medical Devices and Biosensors, 2008
    Co-Authors: Huaming Li, Jindong Tan

    Abstract:

    A novel approach for segmenting ECG signal in a Body Sensor network employing hidden Markov modeling (HMM) technique is presented. In traditional HMM methods inadequate and slow parameter adaptation is largely responsible for the low positive predictivity rate. To solve the problem, we introduce an active HMM parameter adaptation and ECG segmentation algorithm. Body Sensor networks are used to pre-segment the raw ECG data by performing QRS detection. Instead of one single generic HMM, multiple individualized HMMs are used. Each HMM is only responsible for extracting the characteristic waveforms of the ECG signals with similar temporal features from the same group, so that the temporal parameter adaptation can be naturally achieved.

  • Body Sensor network based ECG segmentation and analysis.
    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2007
    Co-Authors: Huaming Li, Jindong Tan

    Abstract:

    In this paper, a Body Sensor network based ECG signal segmentation approach is presented. Hidden Markov Modeling (HMM) technique is employed. Since people’s heart rates vary a lot, the corresponding characteristic waveform intervals and durations change with time as well. For patients with cardiac diseases, such as arrhythmia, the heart beat interval may even change abruptly and irregularly. Because traditional HMM parameter adaptation is conservative and slow to respond to beat interval changes, inadequate and slow parameter adaptation is largely responsible for the low positive predictivity rate (+P). To solve the problem, we introduce an active HMM parameter adaptation and ECG segmentation algorithm, which includes three parts: the pre-segmentation and classification, the HMM model training, and the detailed segmentation. Body Sensor networks are used to collect and pre-segment the raw ECG data by performing QRS detection. Then the R-R interval information that directly reflects the beat variation is extracted and used to classify the raw ECG data into several groups. One specific HMM is trained for each of the groups. Instead of one single generic HMM, multiple individualized HMMs are set up and each HMM is only responsible for extracting the characteristic waveforms of the ECG signals with similar temporal features in the detailed segmentation, so that the temporal parameter adaptation can be naturally achieved.