A comprehensive health assessment framework to facilitate IoT-assisted smart workouts: A predictive healthcare perspective

Bhatia, Munish; Sood, Sandeep K.

doi:10.1016/j.compind.2017.06.009

Cited by 103 publications

(57 citation statements)

References 36 publications

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“…Additional five research papers address the application area Smart health. BI & A research for smart health focuses on supporting nursing and health care processes through the real-time analysis of sensor data (Bhatia and Sood 2017;Chernbumroong et al 2014;Gaber et al 2010). Bourouis et al (2014) introduce a mobile app that detects eye diseases using a small external microscope, connected with a smartphone.…”

Section: Application Areamentioning

confidence: 99%

Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda

Eggert

Alberts

2020

Bus Res

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Researching the field of business intelligence and analytics (BI & A) has a long tradition within information systems research. Thereby, in each decade the rapid development of technologies opened new room for investigation. Since the early 1950s, the collection and analysis of structured data were the focus of interest, followed by unstructured data since the early 1990s. The third wave of BI & A comprises unstructured and sensor data of mobile devices. The article at hand aims at drawing a comprehensive overview of the status quo in relevant BI & A research of the current decade, focusing on the third wave of BI & A. By this means, the paper’s contribution is fourfold. First, a systematically developed taxonomy for BI & A 3.0 research, containing seven dimensions and 40 characteristics, is presented. Second, the results of a structured literature review containing 75 full research papers are analyzed by applying the developed taxonomy. The analysis provides an overview on the status quo of BI & A 3.0. Third, the results foster discussions on the predicted and observed developments in BI & A research of the past decade. Fourth, research gaps of the third wave of BI & A research are disclosed and concluded in a research agenda.

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Section: Application Areamentioning

confidence: 99%

Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda

Eggert

Alberts

2020

Bus Res

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“…A workout can be defined by the following four components (Bhatia & Sood, ): Intensity: degree of effort required by the exercise. It can be calculated objectively by means of a stress test or subjectively using the Börg scale, which measures the effort feeling of the person doing the exercise. Duration: amount of time doing exercise.…”

Section: Related Workmentioning

confidence: 99%

A multimodal conversational coach for active ageing based on sentient computing and m‐health

2019

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As life expectancy increases, it has become more necessary to find ways to support healthy ageing. A number of active ageing initiatives are being developed nowadays to foster healthy habits in the population. This paper presents our contribution to these initiatives in the form of a multimodal conversational coach that acts as a coach for physical activities. The agent can be developed as an Android app running on smartphones and coupled with cheap widely available sport sensors in order to provide meaningful coaching. It can be employed to prepare exercise sessions, provide feedback during the sessions, and discuss the results after the exercise. It incorporates an affective component that informs dynamic user models to produce adaptive interaction strategies.

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“…[49,9]. devices such as smart phones and mobile GIS interfaces [9,67,47,41,12,54]. We consider this technology for integrating it with health GIS for better management of geospatial health data.…”

Section: Fog Computingmentioning

confidence: 99%

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

Barik

Dubey

Mankodiya

et al. 2018

J Ambient Intell Human Comput

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Spatial Data Infrastructure (SDI) is an important framework for sharing geospatial big data using the web. Integration of SDI with cloud computing lead to emergence of Cloud-SDI as a tool for transmission, processing and analysis of geospatial data. Fog computing is a paradigm where embedded devices are employed to increase the throughput and reduce latency at the edge of network. In this study, we developed and evaluated a Fog-based SDI framework named GeoFog4Health for mining analytics from geo-health big data. We built a prototype using both Intel Edison and Raspberry Pi for performing a comparative study. We performed a case study on Malaria vector borne disease positive maps of Maharastra state in India. The proposed framework had provision of lossless data compression. Also, overlay analysis of geospatial data could be performed. In addition, we discussed energy saving, cost analysis and scalability of proposed framework for efficient data processing. We compared the performance of proposed framework with state of the art Cloud-SDI in terms of analysis time. Results and discussions showed the efficacy of proposed system for enhanced analysis of geo-health big data generated from a variety of sensing frameworks.

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A comprehensive health assessment framework to facilitate IoT-assisted smart workouts: A predictive healthcare perspective

Cited by 103 publications

References 36 publications

Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda

Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda

A multimodal conversational coach for active ageing based on sentient computing and m‐health

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

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