Human activity recognition for emergency first responders via body-worn inertial sensors

Every year over 75 000 firefighters are injured and 159 die in the line of duty. Some of these accidents could be averted if first response team leaders had better information about the situation on the ground. The SAFESENS project is developing a novel monitoring system for first responders designed to provide response team leaders with timely and reliable information about their firefighters' status during operations, based on data from wireless inertial measurement units. In this paper we investigate if Gradient Boosted Trees (GBT) could be used for recognising 17 activities, selected in consultation with first responders, from inertial data. By arranging these into more general groups we generate three additional classification problems which are used for comparing GBT with k-Nearest Neighbours (kNN) and Support Vector Machines (SVM). The results show that GBT outperforms both kNN and SVM for three of these four problems with a mean absolute error of less than 7%, which is distributed more evenly across the target activities than that from either kNN or SVM

A generalized model for indoor location estimation using environmental sound from human activity recognition

The indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (eg, hospitals) require location information of their users/patients to offer the services that are needed at the correct moment. Several approaches have been proposed to tackle this problem. In this paper, we present the development of an indoor location system which relies on the human activity recognition approach, using sound as an information source to infer the indoor location based on the contextual information of the activity that is realized at the moment. In this work, we analyze the sound information to estimate the location using the contextual information of the activity. A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the location of the user. We evaluate the quality of the resulting model in terms of sensitivity and specificity for each location, and we also perform out-of-bag error estimation. Our experiments were carried out in five representative residential homes. Each home had four individual indoor rooms. Eleven activities (brewing coffee, cooking, eggs, taking a shower, etc.) were performed to provide the contextual information. Experimental results show that developing an indoor location system (ILS) that uses contextual information from human activities (identified with data provided from the environmental sound) can achieve an estimation that is 95% correct

A Review of Physical Human Activity Recognition Chain Using Sensors

In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.

LASSO regression for monitoring patients progress following ACL reconstruction via motion sensors: a case study

Inertial data can represent a rich source of clinically relevant information, which can provide details on motor assessment in subjects undertaking a rehabilitation process. Indeed, in clinical and sport settings, motor assessment is generally conducted through simple subjective measures such as a visual assessment or questionnaire given by caregivers. Thus, inertial sensor technology and associated data sets can help provide an objective and empirical measure of a patient’s progress. In this publication, several metrics in different domains have been considered and extrapolated from the three-dimensional accelerometer and angular rate data sets collected on an impaired subject with knee injury, via a wearable sensing system developed at the Tyndall National Institute. These data sets were collected for different activities performed across a number of sessions as the subject progressed through the rehabilitation process. Using these data sets and adopting a combination of techniques (LASSO, elastic net regularization, screening-based approaches, and leave-one-out cross-validation), an automated method has been defined in order to select the most suitable features which could provide accurate quantitative analysis of the improvement of the subject throughout their rehabilitation. The present work confirms that changes in motor ability can be objectively assessed via data-driven methods and that most of the alterations of interest occur on the sagittal plane and may be assessed by an accelerometer worn on the thigh

First responders occupancy, activity and vital signs monitoring - SAFESENS

This paper describes the development and implementation of the SAFESENS (Sensor Technologies for Enhanced Safety and Security of Buildings and its Occupants) location tracking and first responder monitoring demonstrator. An international research collaboration has developed a stateof-the-art wireless indoor location tracking system for first responders, focused initially on fire fighter monitoring. Integrating multiple gas sensors and presence detection technologies with building safety sensors and personal monitors has resulted in more accurate and reliable fire and occupancy detection information. This is invaluable to firefighters in carrying out their duties in hostile environments. This demonstration system is capable of tracking occupancy levels in an indoor environment as well as the specific location of fire fighters within those buildings, using a multi-sensor hybrid tracking system. This ultra-wideband indoor tracking system is one of the first of itsâ kind to provide indoor localization capability to sub meter accuracies with combined Bluetooth low energy capability for low power communications and additional inertial, temperature and pressure sensors. This facilitates increased precision in accuracy detection through data fusion, as well as the capability to communicate directly with smartphones and the cloud, without the need for additional gateway support. Glove based, wearable technology has been developed to monitor the vital signs of the first responder and provide this data in real time. The helmet mounted, wearable technology will also incorporate novel electrochemical sensors which have been developed to be able to monitor the presence of dangerous gases in the vicinity of the firefighter and again to provide this information in real time to the fire fighter controller. A SAFESENS demonstrator is currently deployed in Tyndall and is providing real time occupancy levels of the different areas in the building, as well as the capability to track the location of the first responders, their health and the presence of explosive gases in their vicinity. This paper describes the system building blocks and results obtained from the first responder tracking system demonstrator depicted

Wearable Technology Supported Home Rehabilitation Services in Rural Areas:– Emphasis on Monitoring Structures and Activities of Functional Capacity Handbook

The sustainability of modern healthcare systems is under threat. – the ageing of the population, the prevalence of chronic disease and a need to focus on wellness and preventative health management, in parallel with the treatment of disease, pose significant social and economic challenges. The current economic situation has made these issues more acute. Across Europe, healthcare expenditure is expected to rice to almost 16% of GDP by 2020. (OECD Health Statistics 2018). Coupled with a shortage of qualified personnel, European nations are facing increasing challenges in their ability to provide better-integrated and sustainable health and social services. The focus is currently shifting from treatment in a care center to prevention and health promotion outside the care institute. Improvements in technology offers one solution to innovate health care and meet demand at a low cost. New technology has the potential to decrease the need for hospitals and health stations (Lankila et al., 2016. In the future the use of new technologies – including health technologies, sensor technologies, digital media, mobile technology etc. - and digital services will dramatically increase interaction between healthcare personnel and customers (Deloitte Center for Health Solutions, 2015a; Deloitte Center for Health Solutions 2015b). Introduction of technology is expected to drive a change in healthcare delivery models and the relationship between patients and healthcare providers. Applications of wearable sensors are the most promising technology to aid health and social care providers deliver safe, more efficient and cost-effective care as well as improving people’s ability to self-manage their health and wellbeing, alert healthcare professionals to changes in their condition and support adherence to prescribed interventions. (Tedesco et al., 2017; Majumder et al., 2017). While it is true that wearable technology can change how healthcare is monitored and delivered, it is necessary to consider a few things when working towards the successful implementation of this new shift in health care. It raises challenges for the healthcare systems in how to implement these new technologies, and how the growing amount of information in clinical practice, integrates into the clinical workflows of healthcare providers. Future challenges for healthcare include how to use the developing technology in a way that will bring added value to healthcare professionals, healthcare organizations and patients without increasing the workload and cost of the healthcare services. For wearable technology developers, the challenge will be to develop solutions that can be easily integrated and used by healthcare professionals considering the existing constraints. This handbook summarizes key findings from clinical and laboratory-controlled demonstrator trials regarding wearables to assist rehabilitation professionals, who are planning the use of wearable sensors in rehabilitation processes. The handbook can also be used by those developing wearable sensor systems for clinical work and especially for use in hometype environments with specific emphasis on elderly patients, who are our major health care consumers