scholarly journals The Promise and Perils of Wearable Sensors in Organizational Research

2016 ◽  
Vol 20 (1) ◽  
pp. 3-31 ◽  
Author(s):  
Daniel Chaffin ◽  
Ralph Heidl ◽  
John R. Hollenbeck ◽  
Michael Howe ◽  
Andrew Yu ◽  
...  

Rapid advances in mobile computing technology have the potential to revolutionize organizational research by facilitating new methods of data collection. The emergence of wearable electronic sensors in particular harbors the promise of making the large-scale collection of high-resolution data related to human interactions and social behavior economically viable. Popular press and practitioner-oriented research outlets have begun to tout the game-changing potential of wearable sensors for both researchers and practitioners. We systematically examine the utility of current wearable sensor technology for capturing behavioral constructs at the individual and team levels. In the process, we provide a model for performing validation work in this new domain of measurement. Our findings highlight the need for organizational researchers to take an active role in the development of wearable sensor systems to ensure that the measures derived from these devices and sensors allow us to leverage and extend the extant knowledge base. We also offer a caution regarding the potential sources of error arising from wearable sensors in behavioral research.

BioTechniques ◽  
2021 ◽  
Author(s):  
Asami Ito-Masui ◽  
Eiji Kawamoto ◽  
Ryo Esumi ◽  
Hiroshi Imai ◽  
Motomu Shimaoka

Wearable sensor technology enables objective data collection of direct human interactions. The authors review sociometric wearable devices (SWD) and their application in healthcare. Human interactions captured by wearable sensors have been shown to correlate with social constructs such as teamwork and productivity in the office. Application of SWD in the field of healthcare requires special considerations: validation studies have shown technological disadvantages in acute medical settings. Application of SWD in healthcare should be considered based on the strengths and weaknesses of the methodology. SWD can also play an important role in investigation of human interaction and epidemic spread. When study designs and methodologies are carefully considered, incorporation of SWD in healthcare research has promising potential for new insights.


2017 ◽  
Author(s):  
Peter Düking ◽  
Franz Konstantin Fuss ◽  
Hans-Christer Holmberg ◽  
Billy Sperlich

UNSTRUCTURED Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies.


2019 ◽  
Vol 26 (6) ◽  
pp. 1643-1648
Author(s):  
Yuliyan Velkov

A paradox has been established in the modern healthcare industry - consumers can choose between many alternatives but with high uncertainty, while healthcare establishments have numerous possibilities, but they function in conditions of rigorous demand, globalization and large-scale technological efficiency. This requires a re-evaluation of the classical understanding of competition in value creation - healthcare effects (for patients) and financial gains (for the performance of medical and related activities). Today, competition can be explained as a competition for the creation, supply and realization of healthcare products and related services and goods. It is a dynamic process of competition and, in a more general sense, interaction between competing subjects under conditions of significant state interference. It reflects the modern perceptions of health, the improvement of biotechnology and pharmacy, the changed role of the patients - more and more informed, educated, active and united in thematic groups. For the realization with a focus on personal patient preferences, this embodies the characteristics of the interaction between the healthcare establishment and the patient. Competition integrates business logic and patient thinking. In the context of the concept of joint value creation, it covers the intense interactions between healthcare institutions and the individual. Competition in the healthcare industry is based on dialogue, access, risk assessment and transparency at every stage of value creation and realization. This is realized as a competitive interaction in the environment (network) from the influences of healthcare institutions and other producers of medical and non-medical services and goods, thematic associations and regulations. This is a rivalry in creating and offering healthcare products tailored to individual patient's views, preferences, expectations and financial capabilities. The prospects for a competitive race are a transition from competitiveness to competitive interaction. In parallel with the improvement of the operational efficiency of the medical institution, this imposes, the increasing individualization of the created healthcare products. This requires the development of an environment for shared healthcare experiences with the customer. Thus, the development of competition is connected with the realization of the competitive potential of the healthcare establishment through the prism of patient choice - joint creation of healthcare experience through many channels, through options, through transactions and at an appropriate price-to-experience ratio. Consequently, the competitiveness targeting passive patients in need of treatment is shifted from an effective healthcare establishment-to-patient interaction in order to jointly provide patient satisfaction. Competition is a race between dependant healthcare establishments; it is a rivalry between producers of healthcare effects interacting with patients among many environmental influences. Contemporary competition in the healthcare industry is a mechanism for jointly creating healthcare effects by interaction between a healthcare establishment and a patient with the active role of those in need of treatment. This is realized in the form of competition and co-operation in the course of the creation of individualized healthcare experiences. Competition combines a variety of subjective patient needs, medicinal product characteristics, and network experience qualities. As a guideline for improving competition, we can point to enhancing the quality of the environment, enhancing the possibility to take into account patient need heterogeneity, increasing adaptability to changes in demand, and enhancing capabilities to mobilize all potential competencies.


2020 ◽  
Author(s):  
Meera Joshi ◽  
Stephanie Archer ◽  
Abigail Morbi ◽  
Sonal Arora ◽  
Richard Kwasnicki ◽  
...  

BACKGROUND Continuous vital sign monitoring using wearable sensors may enable earlier detection of patient deterioration and sepsis. OBJECTIVE To explore patient experiences of wearable sensor technology and continuous monitoring through questionnaire and interview studies. METHODS All patients recruited for a wearable sensor study were asked to complete a study questionnaire. Patients were asked 9 questions with answers on Likert scale and scores were treated as continuous variables. A subgroup of surgical patients wearing the wearable sensor were invited to take part in semi-structured interviews. All interview data was analysed using thematic analysis. RESULTS A total of 453 patients completed the patient questionnaire (90.6% response rate). A high proportion of patients agreed the wearable sensor was comfortable to wear (n=427, 85.4%), they would wear the patch again when in hospital (n=429, 85.8%) and they would wear the wearable patch at home (n=398, 79.6%). Twelve surgical patients consented to interviews. Five main themes of interest to patients emerged from the interviews; 1) Centralised monitoring 2) enhanced feelings of patient safety, 3) impact on nursing staff 4) comfort & usability and 5) the future and views on technology. CONCLUSIONS Overall, the feedback from patients using wearable monitoring was strongly positive with relatively few concerns raised. Patients feel wearable sensors improve their sense of safety, may relieve pressure on healthcare staff and are a welcome part of future healthcare


Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4962
Author(s):  
Ingrid Eitzen ◽  
Julie Renberg ◽  
Hilde Færevik

Shock impacts during activity may cause damage to the joints, muscles, bones, or inner organs. To define thresholds for tolerable impacts, there is a need for methods that can accurately monitor shock impacts in real-life settings. Therefore, the main aim of this scoping review was to present an overview of existing methods for assessments of shock impacts using wearable sensor technology within two domains: sports and occupational settings. Online databases were used to identify papers published in 2010–2020, from which we selected 34 papers that used wearable sensor technology to measure shock impacts. No studies were found on occupational settings. For the sports domain, accelerometry was the dominant type of wearable sensor technology utilized, interpreting peak acceleration as a proxy for impact. Of the included studies, 28 assessed foot strike in running, head impacts in invasion and team sports, or different forms of jump landings or plyometric movements. The included studies revealed a lack of consensus regarding sensor placement and interpretation of the results. Furthermore, the identified high proportion of validation studies support previous concerns that wearable sensors at present are inadequate as a stand-alone method for valid and accurate data on shock impacts in the field.


Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7693
Author(s):  
Zhaoxi Zhang ◽  
Prince Michael Amegbor ◽  
Clive Eric Sabel

The ever-growing development of sensor technology brings new opportunities to investigate impacts of the outdoor environment on human health at the individual level. However, there is limited literature on the use of multiple personalized sensors in urban environments. This review paper focuses on examining how multiple personalized sensors have been integrated to enhance the monitoring of co-exposures and health effects in the city. Following PRISMA guidelines, two reviewers screened 4898 studies from Scopus, Web of Science, ProQuest, Embase, and PubMed databases published from January 2010 to April 2021. In this case, 39 articles met the eligibility criteria. The review begins by examining the characteristics of the reviewed papers to assess the current situation of integrating multiple sensors for health and environment monitoring. Two main challenges were identified from the quality assessment: choosing sensors and integrating data. Lastly, we propose a checklist with feasible measures to improve the integration of multiple sensors for future studies.


Author(s):  
Alison Keogh ◽  
Kristin Taraldsen ◽  
Brian Caulfield ◽  
Beatrix Vereijken

Abstract Background The use of wearable sensor technology to collect patient health data, such as gait and physical activity, offers the potential to transform healthcare research. To maximise the use of wearable devices in practice, it is important that they are usable by, and offer value to, all stakeholders. Although previous research has explored participants’ opinions of devices, to date, limited studies have explored the experiences and opinions of the researchers who use and implement them. Researchers offer a unique insight into wearable devices as they may have access to multiple devices and cohorts, and thus gain a thorough understanding as to how and where this area needs to progress. Therefore, the aim of this study was to explore the experiences and opinions of researchers from academic, industry and clinical contexts, in the use of wearable devices to measure gait and physical activity. Methods Twenty professionals with experience using wearable devices in research were recruited from academic, industry and clinical backgrounds. Independent, semi-structured interviews were conducted, audio-recorded and transcribed. Transcribed texts were analysed using inductive thematic analysis. Results Five themes were identified: (1) The positives and negatives of using wearable devices in research, (2) The routine implementation of wearable devices into research and clinical practice, (3) The importance of compromise in protocols, (4) Securing good quality data, and (5) A paradigm shift. Researchers overwhelmingly supported the use of wearable sensor technology due to the insights that they may provide. Though barriers remain, researchers were pragmatic towards these, believing that there is a paradigm shift happening in this area of research that ultimately requires mistakes and significant volumes of further research to allow it to progress. Conclusions Multiple barriers to the use of wearable devices in research and clinical practice remain, including data management and clear clinical utility. However, researchers strongly believe that the potential benefit of these devices to support and create new clinical insights for patient care, is greater than any current barrier. Multi-disciplinary research integrating the expertise of both academia, industry and clinicians is a fundamental necessity to further develop wearable devices and protocols that match the varied needs of all stakeholders.


Author(s):  
Nicholas Skapura ◽  
Guozhu Dong

Understanding diseases and human activities, and constructing highly accurate classifiers are two important tasks in bio-medicine, healthcare, and wearable sensor technology. Being able to mine high-quality patterns is useful here, as such patterns can help improve understanding and build accurate classifiers. However, most pattern mining algorithms only operate on discrete data; applying them often requires a binning step to discretize continuous attributes. This article presents a new discretization technique, called Class Distribution Curve based Binning (CDC Binning); the main idea is to use a so-called class distribution curve, which measures the class purity in sliding windows over an attribute's range, to construct binning intervals. Experiments show that (1) CDC Binning outperforms existing binning methods in discovering high-quality patterns, especially when the class distribution curve is complicated (e.g. when the two classes are two fairly similar human activities), and (2) it can outperform other binning methods by 10% in classification accuracy when using discovered patterns as features. CDC Binning is particularly useful for applications where the classes/activities to be distinguished are similar to each other. This is especially important in wearable sensor technology where detection of behavioral or activity changes in a person (e.g. fall detection) could indicate a significant medical event.


2016 ◽  
Author(s):  
Sohrab Saeb ◽  
Luca Lonini ◽  
Arun Jayaraman ◽  
David C. Mohr ◽  
Konrad P. Kording

AbstractThe availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map that data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is important to reliably quantify their prediction accuracy. Cross-validation is the standard approach for evaluating the accuracy of such algorithms; however, several cross-validations methods exist and only some of them are statistically meaningful. Here we compared two popular cross-validation methods: record-wise and subject-wise. Using both a publicly available dataset and a simulation, we found that record-wise cross-validation often massively overestimates the prediction accuracy of the algorithms. We also found that this erroneous method is used by almost half of the retrieved studies that used accelerometers, wearable sensors, or smartphones to predict clinical outcomes. As we move towards an era of machine learning based diagnosis and treatment, using proper methods to evaluate their accuracy is crucial, as erroneous results can mislead both clinicians and data scientists.


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