Transportation mode recognition fusing wearable motion, sound and vision sensors

We present the first work that investigates the potential of improving the performance of transportation mode recognition through fusing multimodal data from wearable sensors: motion, sound and vision. We first train three independent deep neural network (DNN) classifiers, which work with the three types of sensors, respectively. We then propose two schemes that fuse the classification results from the three mono-modal classifiers. The first scheme makes an ensemble decision with fixed rules including Sum, Product, Majority Voting, and Borda Count. The second scheme is an adaptive fuser built as another classifier (including Naive Bayes, Decision Tree, Random Forest and Neural Network) that learns enhanced predictions by combining the outputs from the three mono-modal classifiers. We verify the advantage of the proposed method with the state-of-the-art Sussex-Huawei Locomotion and Transportation (SHL) dataset recognizing the eight transportation activities: Still, Walk, Run, Bike, Bus, Car, Train and Subway. We achieve F1 scores of 79.4%, 82.1% and 72.8% with the mono-modal motion, sound and vision classifiers, respectively. The F1 score is remarkably improved to 94.5% and 95.5% by the two data fusion schemes, respectively. The recognition performance can be further improved with a post-processing scheme that exploits the temporal continuity of transportation. When assessing generalization of the model to unseen data, we show that while performance is reduced - as expected - for each individual classifier, the benefits of fusion are retained with performance improved by 15 percentage points. Besides the actual performance increase, this work, most importantly, opens up the possibility for dynamically fusing modalities to achieve distinct power-performance trade-off at run time

Proceedings of the 1st joint workshop on Smart Connected and Wearable Things 2016

These are the Proceedings of the 1st joint workshop on Smart Connected and Wearable Things (SCWT'2016, Co-located with IUI 2016). The SCWT workshop integrates the SmartObjects and IoWT workshops. It focusses on the advanced interactions with smart objects in the context of the Internet-of-Things (IoT), and on the increasing popularity of wearables as advanced means to facilitate such interactions

Utilising Emotion Monitoring for Developing Music Interventions for People with Dementia:A State-of-the-Art Review

The demand for smart solutions to support people with dementia (PwD) is increasing. These solutions are expected to assist PwD with their emotional, physical, and social well-being. At the moment, state-of-the-art works allow for the monitoring of physical well-being; however, not much attention is delineated for monitoring the emotional and social well-being of PwD. Research on emotion monitoring can be combined with research on the effects of music on PwD given its promising effects. More specifically, knowledge of the emotional state allows for music intervention to alleviate negative emotions by eliciting positive emotions in PwD. In this direction, the paper conducts a state-of-the-art review on two aspects: (i) the effect of music on PwD and (ii) both wearable and non-wearable sensing systems for emotional state monitoring. After outlining the application of musical interventions for PwD, including emotion monitoring sensors and algorithms, multiple challenges are identified. The main findings include a need for rigorous research approaches for the development of adaptable solutions that can tackle dynamic changes caused by the diminishing cognitive abilities of PwD with a focus on privacy and adoption aspects. By addressing these requirements, advancements can be made in harnessing music and emotion monitoring for PwD, thereby facilitating the creation of more resilient and scalable solutions to aid caregivers and PwD

A Model for patient engagement integration in perinatal eHealth development and quality assurance

The aim of this study was to construct a model for patient engagement integration in perinatal eHealth development and quality assurance. The model was developed in four phases. The first three phases produced evidence for the development of a model. In the final phase, a qualitative interpretive synthesis was conducted using grounded theory to articulate a patient engagement model composed of three steps. The first phase was a scoping review aimed at describing the nature and range of patient engagement from the perspective of access, personalization, commitment, and therapeutic alliance within perinatal eHealth. A narrative synthesis was used to describe findings. Phase two consisted of two studies exploring engagement practices of pregnant users during their use of a self-monitoring health promotion eHealth system. A descriptive comparative analysis was completed to understand user engagement patterns based on physical use of the wearable device. A mixed-methods convergence evaluation was conducted to understand the process of accessing the health promotion eHealth system. In phase three a process evaluation tool for parent participation and collaboration (in the neonatal intensive care unit) was developed and psychometrically tested. For the interpretive synthesis, articles from the first three phases of this study were purposively sampled. A deductive codebook was developed using Donabedian’s model, and an adapted version of Lewin’s Action Research Cycle. Donabedian’s model consists of quality assurance through the examination of structure, process, and outcomes. Lewin’s Action Research Cycle informs iterative steps in development and implementation of health systems. Phase four resulted in a model for patient engagement integration in perinatal eHealth development and quality assurance. Three steps of the model were identified as being: Person-centered Perinatal eHealth program mapping; Process evaluation through monitoring of patient engagement processes; and Co-creation of perinatal eHealth programs through real-life testing of perinatal eHealth systems.Malli potilaan osallistumisesta perinataaliajan sähköisen terveydenhuollon kehittämiseen ja laadunvarmistukseen Tutkimuksen tavoitteena oli kehittää malli ohjaamaan potilaan osallistumista perinataaliajan sähköisen terveydenhuollon kehittämiseen ja laadunvarmistukseen. Malli kehitettiin neljässä vaiheessa. Kolmessa ensimmäisessä vaiheessa tuotettiin tutkimusnäyttöä kehittämisen tueksi. Viimeisessä vaiheessa laadullisen tulkitsevan synteesin avulla muodostettiin potilaan sitoutumisen malli. Ensimmäisessä vaiheessa tehtiin kartoittava kirjallisuuskatsaus, joka kuvasi potilaiden sähköiseen terveydenhuoltoon osallistumisen tavat ja laajuuden saatavuuden, yksilöllisyyden, sitoutumisen ja terapeuttisen hoitosuhteen näkökulmasta. Aineisto analysoitiin teorialähtöisellä sisällönanalyysillä ja tulokset kuvattiin narratiivisen synteesin avulla. Toinen vaihe muodostui kahdesta tutkimuksesta, jotka tarkastelivat itsemonitorointisysteemin avulla raskaana olevien henkilöiden osallistumistapoja terveydenedistämiseen. Tutkimuksissa odottajat käyttivät itsemonitorointisysteemiä. Osallistumistapoja analysoitiin puettavan laitteen käyttöajan pohjalta tehtyjen vertailevien analyysien avulla. Monimenetelmällisessä tutkimuksessa muodostettiin analyysin pohjalta ymmärrys itsemonitorointisysteemin saatavuuteen liittyvästä prosessista. Kolmannessa vaiheessa kehitettiin ja psykometrisesti testattiin prosessievaluaatiomittari arvioimaan vanhempien osallistumista ja yhteistyötä henkilökunnan kanssa vastasyntyneiden teho-osastolla. Viimeisen vaiheen tulkitsevaa synteesiä varten valittiin tarkoituksenmukaisia artikkeleita. Donabedianin terveydenhuollon laadunvarmistuksen malli ja Lewinin muokatun toimintatutkimuksen syklin pohjalta muodostettiin teorialähtöinen analyysirunko. Neljännen vaiheen tuloksena muodostettiin malli potilaan osallistumisesta perinataaliajan sähköisen terveydenhuollon kehittämiseen ja laadunvarmistukseen. Malli kostuu kolmesta askeleesta: Yksilökeskeisen sähköisen terveydenhuollon kartoitus, potilaan osallistumisprosessin monitorointiin perustuva prosessievaluaatio ja perinataaliajan sähköisen terveydenhuollon yhteiskehittäminen kliinisessä todellisuudessa

Medical Devices Information Systems in Primary Care

People who suffer from chronic diseases are becoming more involved in remote monitoring processes each year. The market acceptance of remote care programmes, connecting patients through medical devices as part of the treatment regime, is spreading worldwide. Healthcare providers use medical devices to monitor, in various ways, the chronically ill population, namely people with diabetes and hypertension. However, most hospital and service provider information systems do not conform to the same important data standards, making interoperability and information sharing difficult. In this sense, the Multimorbidity Health Information System (METHIS) project is a multidisciplinary, goal-oriented, design-science-based intervention aiming to improve physician-patient communication and patient engagement. It focuses on multimorbidity and ageing, encompassing patients with more than one chronic disease and over 65 years old. The proposed solution is a Clinical Medical Devices Information (CMDI) system and data model which contains standardised information about chronic patients, medical devices and other data sets to be included in the METHIS System. With this framework, the system can perform consistently and reliably while meeting all relevant regulatory requirements or standards. Based on this dissertation and the METHIS project’s complementary work, implementing the CMDI in various Family Health Unit (FHU) in Portugal will make it possible to combat the diversity and loss of telemonitoring information.A cada ano, os doentes crónicos estão mais envolvidos em processos de telemonitorização. A aceitação pelo mercado de programas de cuidados à distância, ligando doentes através de dispositivos médicos como parte do regime de tratamento, está a espalhar-se por todo o mundo. Os prestadores de cuidados de saúde utilizam dispositivos médicos para monitorizar, de várias formas, a população cronicamente doente, nomeadamente as pessoas com diabetes e hipertensão arterial. No entanto, a maioria dos sistemas de informação dos hospitais e prestadores de serviços não estão em conformidade com as mesmas normas, o que dificulta a interoperabilidade e a partilha de informação. Neste sentido, o projeto METHIS é uma intervenção multidisciplinar, baseada em Design Science, que visa melhorar a comunicação entre médico e doente e o envolvimento do mesmo. Tem como foco a multimorbidade e o envelhecimento, englobando doentes com várias doenças crónicas e com idade superior a 65 anos. A solução proposta é um sistema e o correspondente modelo de dados CMDI que contém informação padronizada sobre doentes crónicos, dispositivos médicos e outros conjuntos de dados a serem incluídos no Sistema METHIS. Com este modelo de dados, o sistema possui a informação para poder funcionar de forma consistente e fiável, cumprindo todos os requisitos ou normas regulamentares relevantes. Com base nesta dissertação e no trabalho complementar do projeto METHIS, a implementação da base de dados CMDI em vários Unidades de Saúde em Portugal tornará possível combater a diversidade e a perda de informação na telemonitorização

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation

Building an Understanding of Human Activities in First Person Video using Fuzzy Inference

Activities of Daily Living (ADL’s) are the activities that people perform every day in their home as part of their typical routine. The in-home, automated monitoring of ADL’s has broad utility for intelligent systems that enable independent living for the elderly and mentally or physically disabled individuals. With rising interest in electronic health (e-Health) and mobile health (m-Health) technology, opportunities abound for the integration of activity monitoring systems into these newer forms of healthcare. In this dissertation we propose a novel system for describing ’s based on video collected from a wearable camera. Most in-home activities are naturally defined by interaction with objects. We leverage these object-centric activity definitions to develop a set of rules for a Fuzzy Inference System (FIS) that uses video features and the identification of objects to identify and classify activities. Further, we demonstrate that the use of FIS enhances the reliability of the system and provides enhanced explainability and interpretability of results over popular machine-learning classifiers due to the linguistic nature of fuzzy systems

Stretch sensors for measuring knee kinematics in sports

The popularity of wearable technology in sport has increased, due to its ability to provide unobtrusive monitoring of athletes. This technology has been used to objectively measure kinetic and kinematic variables, with the aim of preventing injury, maximising athletic performance and classifying the skill level of athletes, all of which can influence training and coaching practices. Wearable technologies overcome the limitations of motion capture systems which are limited in their capture volume, enabling the collection of data in-field, during training and competition. Inertial sensors are a common form of technology used in these environments however, their high-cost and complex calibration due to multiple sensor integration can make them prohibitive for widespread use. This thesis focuses on the development of a strain sensor that can be used to measure knee range of motion in sports, specifically rowing and cycling, as a potential low-cost, lightweight alternative to inertial sensors which can also be integrated into clothing, making them more discreet. A systematic review highlighted the lack of alternate technologies to inertial sensors such as strain sensors, as well as the limited use of wearable technologies in both rowing and cycling. Strain sensors were fabricated from a carbon nanotube-natural rubber composite using solvent exchange techniques and employed a piezoresistive sensing mechanism. These were then characterised using mechanical testing, to determine their electrical properties under cyclical strain. The strain sensors displayed hysteretic behaviour, but were durable, withstanding over 4500 strain cycles. Statistical analysis indicated that over 60% of the tests conducted had good intra-test variability with regards to the resistance response range in each strain cycle and sensor response deviating by less than 10% at strain rates below 100 mm/min and less than 20% at a strain rate of 350 mm/min. These sensors were integrated into a wearable sensor system and tested on rowing and cycling cohorts consisting of ten athletes each, to assess the translational use of the strain sensor. This preliminary testing indicated that strain sensors were able to track the motion of the knee during the rowing stroke and cycling pedalling motion, when compared to the output of a motion capture system. Perspectives of participants on the wearable system were collected, which indicated their desire for a system that they could use in their sport, and they considered the translation of this system for real-life use with further development to improve comfort of the system and consistency of the sensor response. The strain sensors developed in this project, when integrated into a wearable sensor system, have the potential to provide an unobtrusive method of measuring knee kinematics, helping athletes, coaches and other support staff make technical changes that can reduce injury risk and improve performance.Open Acces

Design of a man-wearable control station for a robotic rescue system

This report details the design, development, and testing of a man-wearable operator control station for the use of a low-cost robotic system in Urban Search and Rescue (USAR). The complete system, dubbed the "Scarab", is the 1st generation developed and built in the Robotics and Agents Research Laboratory (RARL) at the University of Cape Town (UCT), and was a joint effort between three MSc students. Robots have found a place in USAR as replaceable units which can be deployed into dangerous and confined voids in the place of humans. As such, they have been utilized in a large variety of disaster environments including ground, aerial, and underwater scenarios, and have been gathering research momentum since their first documented deployment in the rescue operations surrounding the 9/11 terrorist attacks. However one issue is their cost as they are not economical solutions, making them less viable for inclusion into a rescue mission as well as negatively affecting the operator‟s decisions in order to prioritise the safety of the unit. Another concern is their difficulty of transport, which becomes dependent on the size and portability of the robot. As such, the Scarab system was conceived to provide a deployable robotic platform which was lowcost, with a budget goal of US $ 500. To address the transportability concerns, it aimed to be portable and light-weight; being able to be thrown through a window by a single hand and withstanding a drop height of 3 m. It includes an internal sensor payload which incorporates an array of sensors and electronics, including temperature monitors and two cameras to provide both a normal and IR video feed. Two LED spotlights are used for navigation, and a microphone and buzzer is included for interaction with any discovered survivors. The operator station acts as the user interface between the operator and the robotic platform. It aimed to be as intuitive as possible, providing quick deployment and minimalizing the training time required for its operation. To further enhance the Scarab system‟s portability, it was designed to be a manwearable system, allowing the operator to carry the robotic platform on their back. It also acts as a charging station, supplying power to the robotic platform‟s on-board charging circuitry. The control station‟s mechanical chassis serves as the man-wearable component of the system, with the functionality being achieved by integration onto a tactical vest. This allows the operator to take the complete system on and off as a single unit without assistance, and uses two mounting brackets to dock the robotic platform. Key areas focussed upon during design were the weight and accessibility of the system, as well as providing a rugged housing for the internal electronics. All parts were manufactured in the UCT Mechanical Engineering workshop