A review of contemporary techniques for measuring ergonomic wear comfort of protective and sport clothing

Protective and sport clothing is governed by protection requirements, performance, and comfort of the user. The comfort and impact performance of protective and sport clothing are typically subjectively measured, and this is a multifactorial and dynamic process. The aim of this review paper is to review the contemporary methodologies and approaches for measuring ergonomic wear comfort, including objective and subjective techniques. Special emphasis is given to the discussion of different methods, such as objective techniques, subjective techniques, and a combination of techniques, as well as a new biomechanical approach called modeling of skin. Literature indicates that there are four main techniques to measure wear comfort: subjective evaluation, objective measurements, a combination of subjective and objective techniques, and computer modeling of human–textile interaction. In objective measurement methods, the repeatability of results is excellent, and quantified results are obtained, but in some cases, such quantified results are quite different from the real perception of human comfort. Studies indicate that subjective analysis of comfort is less reliable than objective analysis because human subjects vary among themselves. Therefore, it can be concluded that a combination of objective and subjective measuring techniques could be the valid approach to model the comfort of textile materials

Ambient health monitoring: the smartphone as a body sensor network component

Inertial measurement units used in commercial body sensor networks (e.g. animation suits) are inefficient, difficult to use and expensive when adapted for movement science applications concerning medical and sports science. However, due to advances in micro-electro mechanical sensors, these inertial sensors have become ubiquitous in mobile computing technologies such as smartphones. Smartphones generally use inertial sensors to enhance the interface usability. This paper investigates the use of a smartphone’s inertial sensing capability as a component in body sensor networks. It discusses several topics centered on inertial sensing: body sensor networks, smartphone networks and a prototype framework for integrating these and other heterogeneous devices. The proposed solution is a smartphone application that gathers, processes and filters sensor data for the purpose of tracking physical activity. All networking functionality is achieved by Skeletrix, a framework for gathering and organizing motion data in online repositories that are conveniently accessible to researchers, healthcare professionals and medical care workers

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

360 Quantified Self

Wearable devices with a wide range of sensors have contributed to the rise of the Quantified Self movement, where individuals log everything ranging from the number of steps they have taken, to their heart rate, to their sleeping patterns. Sensors do not, however, typically sense the social and ambient environment of the users, such as general life style attributes or information about their social network. This means that the users themselves, and the medical practitioners, privy to the wearable sensor data, only have a narrow view of the individual, limited mainly to certain aspects of their physical condition. In this paper we describe a number of use cases for how social media can be used to complement the check-up data and those from sensors to gain a more holistic view on individuals' health, a perspective we call the 360 Quantified Self. Health-related information can be obtained from sources as diverse as food photo sharing, location check-ins, or profile pictures. Additionally, information from a person's ego network can shed light on the social dimension of wellbeing which is widely acknowledged to be of utmost importance, even though they are currently rarely used for medical diagnosis. We articulate a long-term vision describing the desirable list of technical advances and variety of data to achieve an integrated system encompassing Electronic Health Records (EHR), data from wearable devices, alongside information derived from social media data.Comment: QCRI Technical Repor

Affective Medicine: a review of Affective Computing efforts in Medical Informatics

Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

Learning Audio Sequence Representations for Acoustic Event Classification

Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that capture the underlying characteristics of the acoustic events is still challenging. Previous methods mainly focused on designing the audio features in a 'hand-crafted' manner. Interestingly, data-learnt features have been recently reported to show better performance. Up to now, these were only considered on the frame-level. In this paper, we propose an unsupervised learning framework to learn a vector representation of an audio sequence for AEC. This framework consists of a Recurrent Neural Network (RNN) encoder and a RNN decoder, which respectively transforms the variable-length audio sequence into a fixed-length vector and reconstructs the input sequence on the generated vector. After training the encoder-decoder, we feed the audio sequences to the encoder and then take the learnt vectors as the audio sequence representations. Compared with previous methods, the proposed method can not only deal with the problem of arbitrary-lengths of audio streams, but also learn the salient information of the sequence. Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC

Healthcare PANs: Personal Area Networks for trauma care and home care

The first hour following the trauma is of crucial importance in trauma care. The sooner treatment begins, the better the ultimate outcome for the patient. Generally the initial treatment is handled by paramedical personnel arriving at the site of the accident with an ambulance. There is evidence to show that if the expertise of the on-site paramedic team can be supported by immediate and continuous access to and communication with the expert medical team at the hospital, patient outcomes can be improved. After care also influences the ultimate recovery of the patient. After-treatment follow up often occurs in-hospital in spite of the fact that care at home can offer more advantages and can accelerate recovery. Based on emerging and future wireless communication technologies, in a previous paper [1] we presented an initial vision of two future healthcare settings, supported by applications which we call Virtual Trauma Team and Virtual Homecare Team. The Virtual Trauma Team application involves high quality wireless multimedia communications between ambulance paramedics and the hospital facilitated by paramedic Body Area Networks (BANs) [2] and an ambulance-based Vehicle Area Network (VAN). The VAN supports bi-directional streaming audio and video communication between the ambulance and the hospital even when moving at speed. The clinical motivation for Virtual Trauma Team is to increase survival rates in trauma care. The Virtual Homecare Team application enables homecare coordinated by home nursing services and supported by the patient's PAN which consists of a patient BAN in combination with an ambient intelligent home environment. The homecare PAN provides intelligent monitoring and support functions and the possibility to ad hoc network to the visiting health professionals’ own BANs as well as high quality multimedia communication links to remote members of the virtual team. The motivation for Virtual Homecare Team is to improve quality of life and independence for patients by supporting care at home; the economic motivation is to replace expensive hospital-based care with homecare by virtual teams using wireless technology to support the patient and the carers. In this paper we develop the vision further and focus in particular on the concepts of personal and body area networks