Industrial area environmental monitoring based on transducers with MODBUS communication

The monitoring of environmental parameters in industrial areas where potential sources of pollution exist is very important from the point of view of prevention of environmental accidents. In this paper, we propose a solution for the monitoring of the environmental parameters with the local acquisition through specific environmental and movement sensors and data transmission to a higher hierarchical level through the use of MODBUS communications. A flexible hardware structure and software development concept are presented to offer local information and to be integrated into an environmental quality monitoring network.

A Systematic Review of Diagnostic Accuracy and Clinical Applications of Wearable Movement Sensors for Knee Joint Rehabilitation

In clinical practice, only a few reliable measurement instruments are available for monitoring knee joint rehabilitation. Advances to replace motion capturing with sensor data measurement have been made in the last years. Thus, a systematic review of the literature was performed, focusing on the implementation, diagnostic accuracy, and facilitators and barriers of integrating wearable sensor technology in clinical practices based on a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. For critical appraisal, the COSMIN Risk of Bias tool for reliability and measurement of error was used. PUBMED, Prospero, Cochrane database, and EMBASE were searched for eligible studies. Six studies reporting reliability aspects in using wearable sensor technology at any point after knee surgery in humans were included. All studies reported excellent results with high reliability coefficients, high limits of agreement, or a few detectable errors. They used different or partly inappropriate methods for estimating reliability or missed reporting essential information. Therefore, a moderate risk of bias must be considered. Further quality criterion studies in clinical settings are needed to synthesize the evidence for providing transparent recommendations for the clinical use of wearable movement sensors in knee joint rehabilitation.

Smartphone Movement Sensors for Remote Monitoring of Respiratory Rate: Observational Study

BackgroundMobile health offers potential benefits to patients and healthcare systems alike. Existing remote technologies to measure respiratory rate (RR) have limitations, such as cost, accessibility and reliability. Using smartphone sensors to measure RR may offer a potential solution.ObjectiveThe aim of this study was to conduct a comprehensive assessment of a novel mHealth smartphone application designed to measure RR using movement sensors.MethodsIn Study 1, 15 participants simultaneously measured their RR with the app, and an FDA cleared reference device. A novel reference method to allow the app to be evaluated ‘in the wild’ was also developed. In Study 2, 165 participants measured their RR using the app, and these measures were compared to the novel reference. Usability of the app was also assessed in both studies.ResultsThe app, when compared to the FDA-cleared and novel references, respectively, showed a mean absolute error (MAE) of 1.65 (SD=1.49) and 1.14 (1.44), relative MAE of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement (LoA) =-3.27-4.89) and 0.08 (−3.68-3.51). Pearson correlation coefficients were 0.700 and 0.885. 93% of participants successfully operated the app on their first use.ConclusionsThe accuracy and usability of the app demonstrated here show promise for the use of mHealth solutions employing smartphone sensors to remotely monitor RR. Further research should validate the benefits that this technology may offer patients and healthcare systems.

A conversation with Kamela Heyward-Rotimi

Being watched means much more than being seen. This forum investigates information flows of sensing culled from sources as diverse as temperature check and iris scans to sound and movement sensors across terrains. After Veillance discusses how these systems distribute risk unevenly and shape the lives of populations across the globe. --- Sareeta Amrute, Editor

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

Motor disorders are a common and age-related problem in the general community [...]

A conversation with Kamela Heyward-Rotimi

Being watched means much more than being seen. This forum investigates information flows of sensing culled from sources as diverse as temperature check and iris scans to sound and movement sensors across terrains. After Veillance discusses how these systems distribute risk unevenly and shape the lives of populations across the globe. --- Sareeta Amrute, Editor

Smartphone Movement Sensors for Remote Monitoring of Respiratory Rate: Observational Study (Preprint)

BACKGROUND Mobile health (mHealth) offers notable potential clinical and economic benefits to patients and healthcare systems alike. Although respiratory rate (RR) is of great clinical significance, existing remote technologies to measure RR suffer from limitations, such as cost, accessibility and reliability. Using smartphone movement sensors to measure RR may offer a potential solution to these shortcomings. OBJECTIVE The aim of this study was to conduct a comprehensive and ecologically valid assessment of a novel mHealth smartphone application designed to measure RR using movement sensors. METHODS Study 1 offered a preliminary evaluation, in which RR measurements from 15 participants generated via the mHealth app were compared to simultaneous measurements from a reference device cleared by the US Food and Drug Administration (FDA). Participants’ ability to successfully operate the app was also determined. Finally, a novel reference method, that would allow accuracy of the mHealth app to be investigated ‘in the wild’, was assessed for validity against the FDA-cleared reference. In Study 2, 165 participants of balanced demographics remotely downloaded the mHealth app and measured their RR. Measures from the mHealth app were compared to the novel reference that was assessed in Study 1. Usability was quantified based on the proportion of participants that were able to successfully use the app to measure their RR and standardised usability scales. RESULTS Outcomes from Study 1 supported further assessment of the mHealth app, including as assessed by the novel reference. The mHealth app, when compared to the FDA-cleared and novel references, respectively, showed a mean absolute error (MAE) of 1.65 (standard deviation (SD) = 1.49) and 1.14 (1.44), relative MAE of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement (LoA) = -3.27-4.89) and 0.08 (-3.68-3.51). Pearson Product Moment Correlation (PPMC) coefficients were 0.700 and 0.885. 93% of participants could successfully operate the device on their first use and standardised usability scores were above industry averages. CONCLUSIONS The accuracy and usability of the mHealth app demonstrated in this research hold promise for the use of mHealth solutions employing smartphone movement sensors to remotely monitor RR. Considering methodological limitations, further research should be undertaken to more holistically validate the benefits that this technology may offer patients and healthcare systems.