IoT Based Smart Manufacturing system-Case Studies

Manufacturing now a days growing and becoming more complex, automated and computerized. Smart manufacturing is an emerging form of production manufacturing asset of today and in the future with involvement of smart sensors, actuators, communication technology, smart consumer devices like smart phones and tablets and data-intensive modeling. This paper will highlight a review of IoT application in smart manufacturing. Case studies on advanced techniques used in manufacturing industries for different operation such as Monitoring and controlling of smart equipment, IoT based Smart factory connectivity for industries, Hazardous Gas Detection, Electromyogram (EMG) monitoring system, and Tool wears characterization, Defect predictive in a manufacturing system, Machinery Health monitoring are presented

Smart homecare system for health tele-monitoring

© 2007 IEEE. An increasing aged population worldwide puts our medical capabilities to the test. Research and commercial groups are investigating novel ways to care for the aged and chronically ill both in their own homes and in care facilities. This paper describes a prototype we have developed for remote healthcare monitoring. This personalized smart homecare system uses smart phones, wireless sensors, web servers and IP webcams. To illustrate the functionality of the prototype we describe a series of typical tele-health monitoring scenarios

Computational Approaches for Remote Monitoring of Symptoms and Activities

We now have a unique phenomenon where significant computational power, storage, connectivity, and built-in sensors are carried by many people willingly as part of their life style; two billion people now use smart phones. Unique and innovative solutions using smart phones are motivated by rising health care cost in both the developed and developing worlds. In this work, development of a methodology for building a remote symptom monitoring system for rural people in developing countries has been explored. Design, development, deployment, and evaluation of e-ESAS is described. The system’s performance was studied by analyzing feedback from users. A smart phone based prototype activity detection system that can detect basic human activities for monitoring by remote observers was developed and explored in this study. The majority voting fusion technique, along with decision tree learners were used to classify eight activities in a multi-sensor framework. This multimodal approach was examined in details and evaluated for both single and multi-subject cases. Time-delay embedding with expectation-maximization for Gaussian Mixture Model was explored as a way of developing activity detection system using reduced number of sensors, leading to a lower computational cost algorithm. The systems and algorithms developed in this work focus on means for remote monitoring using smart phones. The smart phone based remote symptom monitoring system called e-ESAS serves as a working tool to monitor essential symptoms of patients with breast cancer by doctors. The activity detection system allows a remote observer to monitor basic human activities. For the activity detection system, the majority voting fusion technique in multi-sensor architecture is evaluated for eight activities in both single and multiple subjects cases. Time-delay embedding with expectation-maximization algorithm for Gaussian Mixture Model was studied using data from multiple single sensor cases

Computational Approaches for Remote Monitoring of Symptoms and Activities

We now have a unique phenomenon where significant computational power, storage, connectivity, and built-in sensors are carried by many people willingly as part of their life style; two billion people now use smart phones. Unique and innovative solutions using smart phones are motivated by rising health care cost in both the developed and developing worlds. In this work, development of a methodology for building a remote symptom monitoring system for rural people in developing countries has been explored. Design, development, deployment, and evaluation of e-ESAS is described. The system’s performance was studied by analyzing feedback from users. A smart phone based prototype activity detection system that can detect basic human activities for monitoring by remote observers was developed and explored in this study. The majority voting fusion technique, along with decision tree learners were used to classify eight activities in a multi-sensor framework. This multimodal approach was examined in details and evaluated for both single and multi-subject cases. Time-delay embedding with expectation-maximization for Gaussian Mixture Model was explored as a way of developing activity detection system using reduced number of sensors, leading to a lower computational cost algorithm. The systems and algorithms developed in this work focus on means for remote monitoring using smart phones. The smart phone based remote symptom monitoring system called e-ESAS serves as a working tool to monitor essential symptoms of patients with breast cancer by doctors. The activity detection system allows a remote observer to monitor basic human activities. For the activity detection system, the majority voting fusion technique in multi-sensor architecture is evaluated for eight activities in both single and multiple subjects cases. Time-delay embedding with expectation-maximization algorithm for Gaussian Mixture Model was studied using data from multiple single sensor cases

Personal heart monitoring and rehabilitation system using smart phones

This paper discusses a personalized heart monitoring system using smart phones and wireless (bio) sensors. Based on several scenarios we present the functionality of a prototype we have built. The application is capable of monitoring the health of high risk cardiac patients. The smart phone application analyses in real-time sensor and environmental data and can automatically alert the ambulance and pre assigned caregivers when a heart patient is in danger. It also transmits sensor data to a healthcare centre for remote monitoring by a nurse or cardiologist. The system can be personalized and rehabilitation programs can monitor the progress of a patient. Rehabilitation programs can be used to give advice (e.g. exercise more) or to reassure the patient. © 2006 IEEE

Integration of wearable devices in a wireless sensor network for an e-health application

Applications based on Wireless Sensor Networks for Internet of Things scenarios are on the rise. The multiple possibilities they offer have spread towards previously hard to imagine fields, like e-health or human physiological monitoring. An application has been developed for its usage in scenarios where data collection is applied to smart spaces, aiming at its usage in fire fighting and sports. This application has been tested in a gymnasium with real, non-simulated nodes and devices. A Graphic User Interface has been implemented to suggest a series of exercises to improve a sportsman/woman s condition, depending on the context and their profile. This system can be adapted to a wide variety of e-health applications with minimum changes, and the user will interact using different devices, like smart phones, smart watches and/or tablets

Assistive technologies for the older people: Physical activity monitoring and fall detection

The advancements in information and communications technologies (ICT) and micro-nano manufacturing lead to innovative developments of smart sensors and intelligent devices as well as related assistive technologies which have been directly contributing to improving the life quality, from early detection of diseases to assisting daily living activities. Physical activity monitoring and fall detection are two specific examples where assistive technologies with the use of smart sensors and intelligent devices may play a key role in enhancing the life quality, especially improving the musculoskeletal health which is an essential aspect of health and wellbeing; and it is more important for the older people. This paper presents and dis-cusses about how sensors and wearable devices, such as accelerometers and mobile phones, may be employed to promote the musculoskeletal health. Assistive technologies and methods for physical activity monitoring and fall detection are discussed, with the focus on the fall detection using mobile phone technology, and assessments of the loading intensity of physical activity in a non-laboratory environment. The possible research directions, challenges and potential collaborations in the areas of assistive technologies and ICT solutions for the older populations are proposed and addressed

Technology for Remote Health Monitoring in an Older Population: A Role for Mobile Devices

The impact of an aging population on healthcare and the sustainability of our health care system are pressing issues in contemporary society. Technology has the potential to address these challenges, alleviating pressures on the healthcare system and empowering individuals to have greater control over monitoring their own health. Importantly, mobile devices such as smart phones and tablets can allow older adults to have “on the go” access to health-related information.This paper explores mobile health apps that enable older adults and those who care for them to track health-related factors such as body readings and medication adherence, and it serves as a review of the literature on the usability and acceptance of mobile health apps in an older population

Improving user engagement by aggregating and analysing health and fitness data on a mobile app

© Springer International Publishing Switzerland 2015. Nowadays, health, fitness and contextual data can be ubiquitously collected using wearable devices, sensors and smart phones and be stored in various servers and devices. However, to engage users in active monitoring of their health and fitness, it is essential to personalise the monitoring and have all the relevant data in one place. It is also important to give users control on how their data is collected, analysed, presented and stored. This paper presents how those important features are integrated in myFitnessCompanion®, an Android Health and fitness app developed by our team. The app is able to aggregate data from multiple sources, keep it on the phone or export it to servers or Electronic Health Records (EHR). It can also present the aggregated data in a personalised manner. A mobile app such as myFitnessCompanion® is a solution to the personalisation, interoperability and control issues that are key to user engagement