Catherwood, P

Hiepp, M

McLaughlin, JAD

English

This paper reports on the results of research undertaken to develop a wearable ambulatory fall detector with integrated vitalsigns by implementing a novel algorithm onto a newly CE approved wireless vitalsigns monitor. An ambulatory monitor that alarms on fall events is of clear benefit to the elderly, infirm or those living independently; to enhance their health, sense of safety and wellness. Results show that during laboratory testing, movements categorised as ‘activities of daily living’ (ADL) were detected with around 90% accuracy and falls detected with 100% accuracy, with all data being independently verified using 2 alternative technologies

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Characterisation of accelerometer on an ambulatory patient moni-toring device for fall detection  Philip Catherwood1, Magnus Hiepp1, James McLaughlin1  1 Nanotechnology and Integrated Bioengineering Centre (NIBEC).The University of Ulster at Jordanstown, New-townabbey, Co. Antrim, BT37 0QB, Northern Ireland.  p.catherwood@ulster.ac.uk Summary This paper reports on the results of research undertaken to develop a wearable ambulatory fall detector with inte-grated vitalsigns by implementing a novel algorithm onto a newly CE approved wireless vitalsigns monitor. An ambulatory monitor that alarms on fall events is of clear benefit to the elderly, infirm or those living independent-ly; to enhance their health, sense of safety and wellness. Results show that during laboratory testing, movements categorised as ‘activities of daily living’ (ADL) were detected with around 90% accuracy and falls detected with 100% accuracy, with all data being independently verified using 2 alternative technologies.  Detail For today’s aging society, falls are the leading cause of injury-related deaths and hospitalisation among the elderly.  The faller may often remain on the ground for a considerable amount of time, which occurs in some 20% of hospital admissions. The ability to detect and report falls to a caregiver would result in direct reduc-tion of the ’long lie’ scenario. To address this need a recently CE approved wireless ambulatory patient vi-talsigns monitor, which records ECG, temperature and 3- axial accelerometer data (±6g), has been modified to detect a variety of user movements. In the device’s current form it records accelerometer data and makes it available to clinicians to corroborate specific car-diac events which traditionally exhibit increased pa-tient movement (e.g. hypoglycemia). It has not how-ever been accurately calibrated to detect and distin-guish specific user movements, including trips and falls.   Using a novel algorithm (based on thresholding of re-sultant accelerations), the processed accelerometer data has been proven to determine real-time user ac-tivity, including standing, walking, climbing stairs, posture and various types of falling. Three different types of fall were investigated; forward falls, back-ward falls and sideward falls to both sides (simulated onto crash mats). During laboratory testing, specific user ADL were detected with around 90% accuracy, and falls detected with 100% accuracy. The less than perfect detections for ADL occurred due to occasional issues deciphering between walking/stair walking, as both generate similar accelerometer signals. Addition-ally, some researchers report problems with the sitting down activity being mistaken for a backwards fall. This new algorithm suitably distinguishes between these two events without compromise.  To ensure authenticity of the data recorded, acceler-ometer data from a Shimmer device was recorded and processed simultaneously to validate results, as well as data captured from a specialist high speed video cam-era to reconstruct the movements on the computer af-terwards, using motion analysis software to scrutinise velocity, acceleration and inertia of each test. Sizable datasets also confirm that results are repeatable.  In conclusion, this work presents results of the imple-mentation of a novel algorithm for fall detection on an ambulatory vitalsigns patient monitor and has been proven to detect fall events with 100% accuracy and with no other activity triggering a false negative. This work addresses the real need to help ensure the elderly can live independently, whilst ensuring timely re-sponses to fall events. Future work will include further algorithm development to combine fall detection with other outputs such as heart rate and temperature to in-crease data collected to facilitate post-fall treatment. The next iteration will also implement a Smartphone to enable real-time alerts directly to the caregiver.  Publications [1] P Catherwood, et al. ECG Motion Artefact Re-duction Improvements of a Chest-based Wireless Pa-tient Monitoring System. CinC2010, 26/9/10–29/9/10.  2] R. Harper, P.A. Catherwood, et al. Evaluation of a CE Approved Ambulatory Patient Monitoring De-vice in a General Medical Ward. Engineering in Med-icine and Biology Conference, 31/8/10 – 4/9/10. 

Characterisation of accelerometer on an ambulatory patient monitoringdevice for fall detection

https://pure.ulster.ac.uk/ws/files/11819490/PACatherwood_Intel2010.pdf

Characterisation of accelerometer on an ambulatory patient monitoringdevice for fall detection

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