Smart sensor architecture for vital signs and motor activity monitoring of wheelchair' users

Postolache, Octavian; Freire, J. Costa; Girão, Pedro Silva; Pereira, José Miguel

doi:10.1109/icsenst.2012.6461663

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…In addition to this approach, other works such as Refs. [20,23,29,49] use parameters such as vibration and temperature, where, integrated with RFID technologies to perform performance analyses, they optimize the use of electric motors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This concept is characterized by bringing the preventive maintenance intervention, by predictive methods, to point P of the P-F curve, as found in Ref. [23] and shown in Figure 1. Thus, we can monitor the performance of machines, detect potential problems, and trigger maintenance activities before they become critical.…”

Section: Introductionmentioning

confidence: 99%

“…With this The objective of such predictive maintenance is to reduce maintenance costs and increase the availability of the machine in an attempt to identify various issues in a system from the start of the degradation. Based on this information, the manager can decide which type of maintenance intervention would be better [23].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method

Pereira

Junior

Bandeira

et al. 2023

Sensors

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This paper proposes the use of the AHP-Gaussian method to support the selection of a smart sensor installation for an electric motor used in an escalator in a subway station. The AHP-Gaussian methodology utilizes the Analytic Hierarchy Process (AHP) framework and is highlighted for its ability to save the decision maker’s cognitive effort in assigning weights to criteria. Seven criteria were defined for the sensor selection: temperature range, vibration range, weight, communication distance, maximum electric power, data traffic speed, and acquisition cost. Four smart sensors were considered as alternatives. The results of the analysis showed that the most appropriate sensor was the ABB Ability smart sensor, which scored the highest in the AHP-Gaussian analysis. In addition, this sensor could detect any abnormalities in the equipment’s operation, enabling timely maintenance and preventing potential failures. The proposed AHP-Gaussian method proved to be an effective approach for selecting a smart sensor for an electric motor used in an escalator in a subway station. The selected sensor was reliable, accurate, and cost-effective, contributing to the safe and efficient operation of the equipment.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method

Pereira

Junior

Bandeira

et al. 2023

Sensors

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“…Specifically in chairs or wheelchairs, where physical space is available, and embedding several sensors is possible [16], [17]. Improvements on the state of the art are attainable, as the known implementations on wheelchairs monitor only one cardiac signal, and do not provide a stand alone monitoring solution, or only with limited signal processing data storage and communication capabilities [14,15,26].…”

Section: Introductionmentioning

confidence: 98%

Calibration and validation of homeostasis parameters estimates produced by a DSP embedded in a wheelchair

Pinheiro

Postolache

Girão

2013

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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Ballistocardiogram, capacitive electrocardiogram, and photoplethysmogram (PPG) sensors were embedded in a wheelchair together with a digital signal processor (DSP) with Wi-Fi capabilities. This setup allows monitoring of some homeostasis parameters of the wheelchair user, namely heart rate (HR), pulse arrival and transit times, and oxygen saturation (SpO 2 ). Ten subjects tested the wheelchair capabilities. SpO 2 and HR obtained from the PPG of reference equipment were also recorded for comparative analysis. This paper reports the results of the algorithms developed for HR estimation, for all the signals, and for SpO 2 from the PPG. From the data of the ten subjects the algorithms had their parameters calibrated, and presented an average RMS error of 1.903% for SpO 2 , with the best value being 0.024%. The most unstable signal is the BCG from where the global calibration provides an HR estimate with an average 4.73 bpm error. The algorithms created were confirmed valid, and the signals from the hardware setup can be used to provide estimates with significant accuracy in heart rate estimation, but needing personalized calibration in the SpO 2 case.

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“…SENSOR SUB-SYSTEM MODULEA. Microcontroller-based PlatformThe microcontroller-based platform[14] consists of a PIC 24F128GA010 microcontroller, a 10-bit A/D converter, a 1wire bus interface, and a Universal Asynchronous Receiver/Transmitter (UART) interface, which is responsible to manage biomedical sensors and communicate with host computer. Specifications of these objects under operation are as follows: The microcontroller operates at a frequency of 8MHz  The 10-bit A/D converter sampling rate can reach to maximum at 10kS / s The conditioning circuit must qualified signal amplitude change within 3.3V The digital interface is responsible to transmit all the multi-drop bus information using 1-wire bus  The function of the UART interface is to communicate with the host computer…”

mentioning

confidence: 99%

A Smart Wheelchair Based on Gesture Control and Vital Signs Monitoring

Chi

2015

Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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Aiming to offer better ubiquitous healthcare (Uhealthcare) to the aged and the disabled, this paper introduce a smart wheelchair with embedded sensor system for vital signs which can be controlled by simple gestures. In motion control module, firstly the gesture is distinguished from background using depth images from Kinect. Then three detailed formulas are added to Hu Invariant Moments for hand gesture recognition. Finally, recognition results are transmitted to a host computer via USB 2.0 interface for implementing motion control. In sensor subsystem module, a microcontroller-based platform is designed to process the signals required from biomedical sensors embedded in the wheelchair and output signals are transmitted wirelessly using IEEE802.15.4 WPAN protocol to a server application implemented in a host computer. Physiological parameters are measured by photoplethysmography (PPG), ballistocardiography (BCG) and skin conductivity sensor. Experimental results and analyses show that such a smart wheelchair can provide the aged and disabled people with better U-healthcare.

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Smart sensor architecture for vital signs and motor activity monitoring of wheelchair' users

Cited by 11 publications

References 8 publications

Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method

Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method

Calibration and validation of homeostasis parameters estimates produced by a DSP embedded in a wheelchair

A Smart Wheelchair Based on Gesture Control and Vital Signs Monitoring

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