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. 2013 Nov 16:2013:61-8.
eCollection 2013.

Data collection capabilities of a new non-invasive monitoring system for patients with advanced multiple sclerosis

Diego E Arias  1 , Esteban J Pino  1 , Pablo Aqueveque  1 , Dorothy W Curtis  2
Affiliations

Data collection capabilities of a new non-invasive monitoring system for patients with advanced multiple sclerosis

Diego E Arias et al. AMIA Annu Symp Proc. .

Abstract

This paper reports on a data collection study in a clinical environment to evaluate a new non-invasive monitoring system for people with advanced Multiple Sclerosis (MS) who use powered wheelchairs. The proposed system can acquire respiration and heart activity from ballistocardiogram (BCG) signals, seat and back pressure changes, wheelchair tilt angle, ambient temperature and relative humidity. The data was collected at The Boston Home (TBH), a specialized care residence for adults with advanced MS. The collected data will be used to design algorithms to generate alarms and recommendations for residents and caregivers. These alarms and recommendations will be related to vital signs, low mobility problems and heat exposure. We present different cases where it is possible to illustrate the type of information acquired by our system and the possible alarms we will generate.

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Figures

Figure 1.
Figure 1.
System overview.
Figure 2.
Figure 2.
Deployed sensors on the wheelchair.
Figure 3.
Figure 3.
Power supply, DAH and the laptop are mounted on a wheelchair bag.
Figure 4.
Figure 4.
One of the volunteers using the pulse oximeter during the validation process.
Figure 5.
Figure 5.
Pressure changes during a full day record and tilting (T) episodes that produce seat pressure relief (PR). a) FSR on backrest, b) FSR sensor on seat and c) angle obtained from accelerometer. Black arrows on the FSR sensors mark the beginning and end of the wheelchair occupancy.
Figure 6.
Figure 6.
Subject 1 as the EPW stops (resting) and moves (activity). a) EMFiB and b) accelerometer.
Figure 7.
Figure 7.
Segment of data captured from Subject 2 while resting, watching TV. Processing a) raw EMFiB, it is possible to obtain b) BCG c) and respiration signal.
Figure 8.
Figure 8.
Apnea detected in EMFi sensors.
Figure 9.
Figure 9.
Apnea detected in FSR sensors.
Figure 10.
Figure 10.
Most of the time, Subject 4 does not touch backrest sensors. Only seat sensors are pressed.
Figure 11.
Figure 11.
BCG and respiration signals can only be detected by the EMFiS
See this image and copyright information in PMC

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References

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