Wearable sensor data fusion for remote health assessment and fall detection

Yi, Won-Jae; Sarkar, Oishee; Mathavan, Sivisa; Saniie, Jafar

doi:10.1109/eit.2014.6871782

Cited by 27 publications

(11 citation statements)

References 11 publications

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“…Fusion Level Process Sensors [164] feature-level complementary ECG, accelerometer decision-level cooperative [165] decision-level complementary accelerometer, temperature, ECG cooperative [166] data-level complementary accelerometer, gyroscope, magnetometer feature-level cooperative [168] decision-level complementary heart rate, blood pressure, oxygen saturation, cooperative temperature, EEG, ECG blood sugar, accelerometer Table 9 summarizes some of the features of the most representative studies on the application of sensor fusion in general health. Most of these studies fuse the features or decision extracted from motion sensors and physiological sensors such as EEG, ECG, heart rate, blood pressure, oxygen saturation, body temperature and etc, to remote monitor the health status of the indi-A C C E P T E D M A N U S C R I P T viduals.…”

Section: Referencementioning

confidence: 99%

“…A study in [165] proposed a scalable system for remote user physiological data and movement detection using wearable sensor feature and decision-level data fusion. Their system integrates and analyze the data from body temperature, current geographical location, electrocardiography, body posture and fall detection in real-time to determine user health status like instant heart beat rate, body orientation and possible fall recognition.…”

Section: General-healthmentioning

confidence: 99%

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Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

et al. 2017

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Section: Referencementioning

confidence: 99%

Section: General-healthmentioning

confidence: 99%

Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

et al. 2017

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“…Moreover, physiological devices combined with accelerometers can be considered as a separate subgroup due to specific synchronization requirements and processing of collected measurements. For example, Yi et al [46] deploy temperature sensor and ECG together with accelerometer and perform individual data processing for each device later fused into a unified alert message for medical staff.…”

Section: Wearable Sensors Fusionmentioning

confidence: 99%

Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper

2016

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Emergency situations associated with falls are a serious concern for an aging society. Yet following the recent development within ICT, a significant number of solutions have been proposed to track body movement and detect falls using various sensor technologies, thereby facilitating fall detection and in some cases prevention. A number of recent reviews on fall detection methods using ICT technologies have emerged in the literature and an increasingly popular approach considers combining information from several sensor sources to assess falls. The aim of this paper is to review in detail the subfield of fall detection techniques that explicitly considers the use of multisensor fusion based methods to assess and determine falls. The paper highlights key differences between the single sensor-based approach and a multifusion one. The paper also describes and categorizes the various systems used, provides information on the challenges of a multifusion approach, and finally discusses trends for future work.

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“…Through this profile, binary data and framing techniques can be employed for efficient network communication. In our previous work regarding sensor data collection in the WBSN environment, the system was operating on this profile when collecting body sensor data through the Bluetooth protocol [6]. This protocol was suitable enough for real-time body sensor data transmission.…”

Section: Bluetooth Smartphone Interfacementioning

confidence: 99%

Robust framework for 6LoWPAN-based body sensor network interfacing with smartphone

Gonnot

Monsef

et al. 2015

2015 IEEE International Conference on Electro/Information Technology (EIT)

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This paper presents the design of a robust framework for body sensor network. In this framework, sensor nodes communicate using 6LoWPAN, running on the Contiki operating system, which is designed for energy efficiency and configuration flexibility. Furthermore, an embedded router is implemented using a Raspberry Pi to bridge the information to a Bluetooth capable smartphone. Consequently, the smartphone can process, analyze, compress and send the data to the cloud using its data connection. One of the major application of this framework is home patient monitoring, with 24/7 data collection capability. The collected data can be sent to a doctor at any time, or only when an anomaly is detected.

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Wearable sensor data fusion for remote health assessment and fall detection

Cited by 27 publications

References 11 publications

Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper

Robust framework for 6LoWPAN-based body sensor network interfacing with smartphone

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