Wearable Wireless Sensor Network to Assess Clinical Status in Patients with Neurological Disorders

Lorincz,; Kuris,; Ayer,; Patel,; Bonato,; Welsh,

doi:10.1109/ipsn.2007.4379727

Cited by 8 publications

(5 citation statements)

References 3 publications

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“…O'Donovan et al (2009) reported favorable comparison between gait analysis measured using SHIMMER kinematic sensors and gold standard optical (Codamotion) measurement. The SHIMMER (Barton & Jung, 2009;Lorincz et al, 2007;Patel et al, 2007) sensors are controlled by and communicate data to a master PC via a Bluetooth radio stack. The application software was developed using the BioMOBIUS (2009) software environment that supports data capture at rates up to 100Hz for kinematic SHIMMERs and up to 500Hz for ECG SHIMMERs.…”

Section: Figure 2 Overview Of the Tril Gait Analysis Platformmentioning

confidence: 99%

Falls Prevention in the Home

Kenny

Scanaill

McGrath

2011

Intelligent Technologies for Bridging the Grey Digital Divide

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Approximately 1 in 3 people over the age of 65 fall each year; therefore it is of little surprise that falling is often accepted as a natural part of the aging process. Many falls are simply managed using alarm pendants to notify others when a falls event occurs. However, falls technology extends beyond simple notification; technology can be used to screen for falls risk, or to prevent a fall from occurring. In this chapter, we review the latest best practices for the identification of falls risk. We review the technology, if any, developed to support these practices, and discuss the challenges of using technology for in-home falls prevention, risk assessment and falls detection. Recommendations and suggestions for future research directions are discussed.

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Section: Figure 2 Overview Of the Tril Gait Analysis Platformmentioning

confidence: 99%

Falls Prevention in the Home

Kenny

Scanaill

McGrath

2011

Intelligent Technologies for Bridging the Grey Digital Divide

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“…Sensors became an essential device to collect the data from a physical world. Electronic sensing systems have been utilized in a variety of applications, including biomedical observation, civil engineering monitoring, and energy resource detection [1][2][3][4][5][6][7][8]. Size of the sensors have reduced to fit into a greater number of applications, and they employ batteries to power themselves without external power connection for easier placement [9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Multi-Exit Neural-Network Inferences for an Image-Based Sensing System with Energy Harvesting

Gao

Shao

et al. 2021

JLPEA

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Wireless sensor systems powered by batteries are widely used in a variety of applications. For applications with space limitation, their size was reduced, limiting battery energy capacity and memory storage size. A multi-exit neural network enables to overcome these limitations by filtering out data without objects of interest, thereby avoiding computing the entire neural network. This paper proposes to implement a multi-exit convolutional neural network on the ESP32-CAM embedded platform as an image-sensing system with an energy constraint. The multi-exit design saves energy by 42.7% compared with the single-exit condition. A simulation result, based on an exemplary natural outdoor light profile and measured energy consumption of the proposed system, shows that the system can sustain its operation with a 3.2 kJ (275 mAh @ 3.2 V) battery by scarifying the accuracy only by 2.7%.

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“…While many other new mote platforms have been developed since then (e.g., Iris [4], Epic [6], Shimmer [9], and BTNode [2]) most of these platforms are still based on similar MCUs as the one used on the TelosB described above. Some platforms including the iMote2 [5] provided developers with higher computational power (processing speed up to 416MHz with 256KB of RAM for the iMote2) at the expense of reduced energy efficiency and therefore could not be used in applications that require long-term and unattended deployments.…”

Section: Introductionmentioning

confidence: 99%