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Mobile-Based Patient Monitoring Systems: A Prioritisation Framework Using Multi-Criteria Decision-Making Techniques

Almahdi EM, Zaidan AA, Zaidan BB, Alsalem MA, Albahri OS, Albahri AS

J Med Syst, 2019 Jun 06;43(7):219.
PMID: 31172296 DOI: 10.1007/s10916-019-1339-9

This study presents a prioritisation framework for mobile patient monitoring systems (MPMSs) based on multicriteria analysis in architectural components. This framework selects the most appropriate system amongst available MPMSs for the telemedicine environment. Prioritisation of MPMSs is a challenging task due to (a) multiple evaluation criteria, (b) importance of criteria, (c) data variation and (d) unmeasurable values. The secondary data presented as the decision evaluation matrix include six systems (namely, Yale-National Aeronautics and Space Administration (NASA), advanced health and disaster aid network, personalised health monitoring, CMS, MobiHealth and NTU) as alternatives and 13 criteria (namely, supported number of sensors, sensor front-end (SFE) communication, SFE to mobile base unit (MBU) communications, display of biosignals on the MBU, storage of biosignals on the MBU, intra-body area network (BAN) communication problems, extra-BAN communication problems, extra-BAN communication technology, extra-BAN communication protocols, back-end system communication technology, intended geographic area of use, end-to-end security and reported trial problems) based on the architectural components of MPMSs. These criteria are adopted from the most relevant studies and are found to be applicable to this study. The prioritisation framework is developed in three stages. (1) The unmeasurable values of the MPMS evaluation criteria in the adopted decision evaluation matrix based on expert opinion are represented by using the best-worst method (BWM). (2) The importance of the evaluation criteria based on the architectural components of the MPMS is determined by using the BWM. (3) The VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is utilised to rank the MPMSs according to the determined importance of the evaluation criteria and the adopted decision matrix. For validation, mean ± standard deviation is used to verify the similarity of systematic prioritisations objectively. The following results are obtained. (1) The BWM represents the unmeasurable values of the MPMS evaluation criteria. (2) The BWM is suitable for weighing the evaluation criteria based on the architectural components of the MPMS. (3) VIKOR is suitable for solving the MPMS prioritisation problem. Moreover, the internal and external VIKOR group decision making are approximately the same, with the best MPMS being 'Yale-NASA' and the worst MPMS being 'NTU'. (4) For the objective validation, remarkable differences are observed between the group scores, which indicate the similarity of internal and external prioritisation results.

Matched MeSH terms: Monitoring, Physiologic/instrumentation*
Fulltext Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System

Manogaran G, Shakeel PM, Fouad H, Nam Y, Baskar S, Chilamkurti N, et al.

Sensors (Basel), 2019 Jul 09;19(13).
PMID: 31324070 DOI: 10.3390/s19133030

According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents' physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.

Matched MeSH terms: Monitoring, Physiologic/instrumentation*
Fulltext QOS-aware error recovery in wireless body sensor networks using adaptive network coding

Razzaque MA, Javadi SS, Coulibaly Y, Hira MT

Sensors (Basel), 2014 Dec 29;15(1):440-64.
PMID: 25551485 DOI: 10.3390/s150100440

Wireless body sensor networks (WBSNs) for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS), in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network's QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts.

Matched MeSH terms: Monitoring, Physiologic/instrumentation
Design and development of an automated, portable and handheld tablet personal computer-based data acquisition system for monitoring electromyography signals during rehabilitation

Ahamed NU, Sundaraj K, Poo TS

Proc Inst Mech Eng H, 2013 Mar;227(3):262-74.
PMID: 23662342

This article describes the design of a robust, inexpensive, easy-to-use, small, and portable online electromyography acquisition system for monitoring electromyography signals during rehabilitation. This single-channel (one-muscle) system was connected via the universal serial bus port to a programmable Windows operating system handheld tablet personal computer for storage and analysis of the data by the end user. The raw electromyography signals were amplified in order to convert them to an observable scale. The inherent noise of 50 Hz (Malaysia) from power lines electromagnetic interference was then eliminated using a single-hybrid IC notch filter. These signals were sampled by a signal processing module and converted into 24-bit digital data. An algorithm was developed and programmed to transmit the digital data to the computer, where it was reassembled and displayed in the computer using software. Finally, the following device was furnished with the graphical user interface to display the online muscle strength streaming signal in a handheld tablet personal computer. This battery-operated system was tested on the biceps brachii muscles of 20 healthy subjects, and the results were compared to those obtained with a commercial single-channel (one-muscle) electromyography acquisition system. The results obtained using the developed device when compared to those obtained from a commercially available physiological signal monitoring system for activities involving muscle contractions were found to be comparable (the comparison of various statistical parameters) between male and female subjects. In addition, the key advantage of this developed system over the conventional desktop personal computer-based acquisition systems is its portability due to the use of a tablet personal computer in which the results are accessible graphically as well as stored in text (comma-separated value) form.

Matched MeSH terms: Monitoring, Physiologic/instrumentation*
Fulltext Objective triceps muscle strength measurement using computerized adaptation

Amalourde A, Vinayaga P, Naveed N, Choon SK, Zaleha O

Med J Malaysia, 2004 Dec;59 Suppl F:8-13.
PMID: 15941154

In our centre the non-availability computerized exercise machines limits the objective monitoring of strength rehabilitation. We undertook this research programme to objectively measure triceps muscle strength by interfacing NORSK-Gym machine with accelerometer and positional transducers to a computer. This data was tabulated and processed using Microsoft Excel. The positional transducer was first calibrated and it showed an excellent Pearson Correlation Coefficients against a standard metric reading (r = 0.9999). Peak Force was used as a test parameter for isotonic triceps muscle strength measurements. The criterion-referenced validity was established as the peak forces measured using the accelerometer and positional transducer demonstrated identical Peak Forces (r = 0.94). Analysis of our mean Peak Force measurements using non-biological force as well as the intra-individual reproducibility demonstrated excellent Pearson Correlation Coefficients (r) = 0.982-0.998 and 0.929-0.972 respectively. This computerized adaptation of the NORSK-Gym machine produced an objective, valid and reproducible triceps muscle strength measurement.

Matched MeSH terms: Monitoring, Physiologic/instrumentation
The impact of heart rate-based drowsiness monitoring on adverse driving events in heavy vehicle drivers under naturalistic conditions

Wolkow AP, Rajaratnam SMW, Wilkinson V, Shee D, Baker A, Lillington T, et al.

Sleep Health, 2020 06;6(3):366-373.
PMID: 32340910 DOI: 10.1016/j.sleh.2020.03.005

OBJECTIVES: This study examined the influence of a wrist-worn heart rate drowsiness detection device on heavy vehicle driver safety and sleep and its ability to predict driving events under naturalistic conditions.
DESIGN: Prospective, non-randomized trial.
SETTING: Naturalistic driving in Malaysia.
PARTICIPANTS: Heavy vehicle drivers in Malaysia were assigned to the Device (n = 25) or Control condition (n = 34).
INTERVENTION: Both conditions were monitored for driving events at work over 4-weeks in Phase 1, and 12-weeks in Phase 2. In Phase 1, the Device condition wore the device operated in the silent mode (i.e., no drowsiness alerts) to examine the accuracy of the device in predicting driving events. In Phase 2, the Device condition wore the device in the active mode to examine if drowsiness alerts from the device influenced the rate of driving events (compared to Phase 1).
MEASUREMENTS: All participants were monitored for harsh braking and harsh acceleration driving events and self-reported sleep duration and sleepiness daily.
RESULTS: There was a significant decrease in the rate of harsh braking events (Rate ratio = 0.48, p

Matched MeSH terms: Monitoring, Physiologic/instrumentation*
Fulltext A non-printed integrated-circuit textile for wireless theranostics

Yang Y, Wei X, Zhang N, Zheng J, Chen X, Wen Q, et al.

Nat Commun, 2021 08 12;12(1):4876.
PMID: 34385436 DOI: 10.1038/s41467-021-25075-8

While the printed circuit board (PCB) has been widely considered as the building block of integrated electronics, the world is switching to pursue new ways of merging integrated electronic circuits with textiles to create flexible and wearable devices. Herein, as an alternative for PCB, we described a non-printed integrated-circuit textile (NIT) for biomedical and theranostic application via a weaving method. All the devices are built as fibers or interlaced nodes and woven into a deformable textile integrated circuit. Built on an electrochemical gating principle, the fiber-woven-type transistors exhibit superior bending or stretching robustness, and were woven as a textile logical computing module to distinguish different emergencies. A fiber-type sweat sensor was woven with strain and light sensors fibers for simultaneously monitoring body health and the environment. With a photo-rechargeable energy textile based on a detailed power consumption analysis, the woven circuit textile is completely self-powered and capable of both wireless biomedical monitoring and early warning. The NIT could be used as a 24/7 private AI "nurse" for routine healthcare, diabetes monitoring, or emergencies such as hypoglycemia, metabolic alkalosis, and even COVID-19 patient care, a potential future on-body AI hardware and possibly a forerunner to fabric-like computers.

Matched MeSH terms: Monitoring, Physiologic/instrumentation
Fulltext The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseases

Khalil SF, Mohktar MS, Ibrahim F

Sensors (Basel), 2014;14(6):10895-928.
PMID: 24949644 DOI: 10.3390/s140610895

Bioimpedance analysis is a noninvasive, low cost and a commonly used approach for body composition measurements and assessment of clinical condition. There are a variety of methods applied for interpretation of measured bioimpedance data and a wide range of utilizations of bioimpedance in body composition estimation and evaluation of clinical status. This paper reviews the main concepts of bioimpedance measurement techniques including the frequency based, the allocation based, bioimpedance vector analysis and the real time bioimpedance analysis systems. Commonly used prediction equations for body composition assessment and influence of anthropometric measurements, gender, ethnic groups, postures, measurements protocols and electrode artifacts in estimated values are also discussed. In addition, this paper also contributes to the deliberations of bioimpedance analysis assessment of abnormal loss in lean body mass and unbalanced shift in body fluids and to the summary of diagnostic usage in different kinds of conditions such as cardiac, pulmonary, renal, and neural and infection diseases.

Matched MeSH terms: Monitoring, Physiologic/instrumentation*