119 research outputs found

    Exploring user-defined gestures for alternate interaction space for smartphones and smartwatches

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    2016 Spring.Includes bibliographical references.In smartphones and smartwatches, the input space is limited due to their small form factor. Although many studies have highlighted the possibility of expanding the interaction space for these devices, limited work has been conducted on exploring end-user preferences for gestures in the proposed interaction spaces. In this dissertation, I present the results of two elicitation studies that explore end-user preferences for creating gestures in the proposed alternate interaction spaces for smartphones and smartwatches. Using the data collected from the two elicitation studies, I present gestures preferred by end-users for common tasks that can be performed using smartphones and smartwatches. I also present the end-user mental models for interaction in proposed interaction spaces for these devices, and highlight common user motivations and preferences for suggested gestures. Based on the findings, I present design implications for incorporating the proposed alternate interaction spaces for smartphones and smartwatches

    From Palm to Arm

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    The number of people diagnosed with diabetes is increasing at an alarming rate. However, strong evidence shows that health information technology has improved medical outcomes, especially within the field of diabetes research. This thesis investigates how to motivate people with diabetes to perform self-management activities with the help of a smartwatch application. The work is grounded in a literature review, discovering how people manage diabetes with smartwatches today and the lack of existing motivational features on existing solutions. As a result, a system design of a smartwatch application is presented, including a graphical user interface (UI). The system aims to manage and monitor the essential diabetes metrics: nutrition, blood glucose, and physical activity while generating motivation through goal setting. In addition, the presented system is oriented on a standalone architecture, removing the need to pair a smartphone to the smartwatch and introducing novel features for smartwatch diabetes management. Finally, a proof of concept is implemented using Android studio to solidify the systems requirements. Furthermore, a descriptive analysis of a survey presents that among people with diabetes, simplicity is the most crucial factor in smartwatch applications. Based on this, the presented UI is evaluated according to the simplicity of other systems and the impact the motivational features have on the system’s complexity. Then, the potential of a standalone architecture for diabetes management is discussed. Finally, it is concluded that goal-setting features should be more widely used among smartwatch applications due to their low impact on the application. The future work of the thesis is to test the system on people with diabetes. Both to evaluate the system useability scale and observe the impact goal-setting has on performing diabetes self-management. Furthermore, in this thesis, it is assumed that there is a communication channel between diabetes devices and the smartwatch. This must be further investigated with the next generation of diabetes devices

    Army Hand Signal Recognition System using Smartwatch Sensors

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    The organized armies of the world all have their own hand signal systems to deliver commands and messages between combatants during operations such as search, reconnaissance, and infiltration. For instance, to command a troop to stop, a commander would lift his/her fist next to the his/her face height. When the operation is carried out by a small unit, the hand signal system plays a very important role. However, obviously, there is an aspect of limitation in this method; each signal should be relayed by individuals, which while waiting attentively for a signal can cause soldiers to lose attention on the front observation and be distracted. Another limitation is, it takes a certain period to convey signals from the first person to the last person. While the limitations above are related to a short moment, that can be fatal in the field of battle. Gesture recognition has emerged as a very important and effective way for interaction between human and computer (HCI). An application of inertial measurement unit (IMU) sensor data from smart devices has lead gesture recognition into the next level, because it means people don’t need to rely on any external equipment, such as a camera to read movements. Especially wearable devices can be more adequate for gesture recognition than hand-held devices because of its distinguished strengths. If soldiers can deliver signals using an off-the-shelf smartwatch, without additional training, it can resolve many drawbacks of the current hand signal system. In the battlefield, cameras to record combatants’ movement for image processing cannot be installed nor utilized, and there are countless obstacles, such as tree branches, trunks, or valleys that hinder the camera to observe movements of the combatants. Because of unique characteristics of battlefield, a gesture recognition system using a smartwatch can be the most appropriate solution for making troops mobility more efficient and secure. For the system to be used successfully in combat zone, the system requires high precision and prompt processing; although accuracy and operating speed are inversely proportional in most of cases. This paper will present a gesture recognition tool for army hand signals with high accuracy and fast processing speed. It is expected that the army hand signal recognition system (AHSR) will assist small units to carry-out their maneuver with higher efficiency

    M-CDS: Mobile Carbohydrate Delivery System

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    When patients with type 1 diabetes (T1D) are physically active, they encounter an issue with keeping their blood glucose (BG) stable. Generally, their blood glucose level (BGL) will drop, causing hypoglycaemia which can have fatal consequences. The simple solution is to consume carbohydrates in the form of liquids or food, but during physical activities, it can be difficult to follow their BGL at the same time as they exercise. This thesis presents the design and implementation of a mobile carbohydrate delivery system, M-CDS. Previous work has shown that it is possible to create a stationary carbohydrate delivery system that reads the user’s BG data in real-time, gives feedback to the user when their BGL is nearing hypoglycaemia, and issues a dose of juice with 15 grams of carbohydrates. The proof-of-concept system in this thesis has the same functions but is contained within a modified CamelBak backpack. A Raspberry Pi, together with various sensors and a peristaltic pump, can transfer juice from a drinking reservoir to a drinking tube, which the user can easily drink from while physically active. The results show that the backpack works as intended and was able to avoid a BGL under 3.9 mmol/L while testing the system with a user using physical activity, thus successfully avoiding a hypoglycaemic event. As the system is a proof-of-concept, many things can be improved or modified to create a more robust, user-friendly, compact, and complex system. However, creating a prototype proved to be a time-costly project, whereas future work can use this project as a base to further improve it

    Understanding security risks and users perception towards adopting wearable Internet of Medical Things

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    This thesis examines users’ perception of trust within the context of security and privacy of Wearable Internet of Medical Things (WIoMT). WIoMT is a collective term for all medical devices connected to internet to facilitate collection and sharing of health-related data such as blood pressure, heart rate, oxygen level and more. Common wearable devices include smart watches and fitness bands. WIoMT, a phenomenon due to Internet of Things (IoT) has become prevalent in managing the day-to-day activities and health of individuals. This increased growth and adoption poses severe security and privacy concerns. Similar to IoT, there is a need to analyse WIoMT security risks as they are used by individuals and organisations on regular basis, risking personal and confidential information. Additionally, for better implementation, performance, adoption, and secured wearable medical devices, it is crucial to observe users’ perception. Users’ perspectives towards trust are critical for adopting WIoMT. This research aimed to understand users’ perception of trust in the adoption of WIoMT, while also exploring the security risks associated with adopting wearable IoMT. Employing a quantitative method approach, 189 participants from Western Sydney University completed an online survey. The results of the study and research model indicated more than half of the variance (R2 = 0.553) in the Intention to Use WIoMT devices, which was determined by the significant predictors (95% Confidence Interval; p < 0.05), Perceived Usefulness, Perceived Ease of Use and Perceived Security and Privacy. Among these two, the domain Perceived Security and Privacy was found to have significant outcomes. Hence, this study reinforced that a WIoMT user intends to use the device only if he/she trusts the device; trust here has been defined in terms of its usefulness, easy to use and security and privacy features. This finding will be a steppingstone for equipment vendors and manufacturers to have a good grasp on the health industry, since the proper utilisation of WIoMT devices results in the effective and efficient management of health and wellbeing of users. The expected outcome from this research also aims to identify how users’ security and perception matters while adopting WIoMT, which in future can benefit security professionals to examine trust factors when implementing new and advanced WIoMT devices. Moreover, the expected result will help consumers as well as different healthcare industry to create a device which can be easily adopted and used securely by consumers

    Physiopad: development of a non-invasive game controller toolkit to study physiological responses for Game User Research

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    Os jogos afectivos usam as respostas fisiológicas do jogador para criar um ambiente adequado ao estado emocional do utilizador. A investigação destes jogos tem sido explorada nos últimos anos. Estas experiências, contudo, ainda requerem sistemas complexos e difíceis de utilizar. Nesta dissertação, é proposta a construção de um dispositivo capaz de ler dados fisiológicos de forma não invasiva e que seja de fácil utilização. Este aparelho faz a leitura do ritmo cardíaco e dos níveis de excitação do jogador, além disso foi criado um software para interligar com o dispositivo. Utilizando um comando da PlayStation 3 e um BITalino, o dispositivo é capaz de fazer a aquisição do sinal PPG e sinal EDA durante o jogo. O software analisa os sinais do comando, calcula o ritmo cardíaco e mede os níveis de excitação em tempo real. Foi realizada uma experiência utilizando biofeedback positivo e negativo, com o objectivo de testar a integração entre o software e o hardware. Não será no imediato que os dispositivos deste género sejam disponibilizados comercialmente. Os resultados são, no entanto, promissores. O cálculo do ritmo cardíaco em tempo real tem apenas uma diferença de 5 batimentos por minuto em relação ao ritmo cardíaco real do jogador. Apesar de os testes com o EDA serem inconclusivos, pode-se verificar que foi possível construir um sistema para ler os dados fisiológicos sendo mais económico do que os seus pares, sem comprometer a fiabilidade dos dados.Affective games are a genre of games that use the physiological responses from the player to adapt the gameplay to a more enjoyable emotional state and experience. Physiological responses and affective games have been studied vastly over the years. However, the setups used in these interventions are very intrusive and are complex to set up. In this project, it is purposed to build a non-invasive and easy-to-set-up toolkit that records physiological data. This toolkit records the player's heart rate and arousal levels and was decomposed into software and hardware. Using a PS3 game controller replica and a BITalino, a physiological game controller which can record heart rate and arousal during gameplay was built. The software interfaces with the gamepad, processes the physiological signals and sends this information to the game. An experiment with a positive biofeedback condition and negative biofeedback condition was conducted. This experiment showed that even though more work must be done until these type of devices could be commercially available, the results are promising. This toolkit’s heart rate values, when compared with other more traditional acquisition devices, were very similar, being on average only 5 BMP lower than the actual heart rate, proving that is possible to build more affordable non-invasive physiological hardware without compromising the signal's accuracy

    Personal sensor wristband for smart infrastructure and control

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    Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, February 2013."February 2013." Cataloged from PDF version of thesis.Includes bibliographical references (p. 67-72).Despite the rapid expansion of computers beyond desktop systems into devices and systems in the environment around us, the control interfaces to these systems are often basic and inadequate, particularly for infrastructure systems. WristQue is a wearable interface for interacting with computerized systems in the environment, providing both explicit remote control with buttons, touch, and gestural interfaces, and automatic closed-loop control using environmental sensors on the device, fused with precise indoor location for context. By placing these sensors and controls on the wrist, they are generally able to sense the environment unobstructed and are conveniently within reach at all times. WristQue is able to continuously collect and stream sensor data through a wireless network infrastructure, including temperature, humidity, activity, light, and color. A 9-DoF inertial/ magnetic measurement unit can be enabled to use the WristQue as a wrist-based gestural interface to nearby devices. Location and orientation data is used to implement a pointing interface that the user can use to indicate a device to control. This interface was implemented and tested using the WristQue and a commercial UWB localization system. The other sensors on the WristQue were validated by collecting several days of environmental data and conducting several controlled experiments. With these capabilities, the WristQue can be used in a number of sensing and control applications, such as lighting and comfort control.by Brian D. Mayton.S.M

    e-NABLE: DIY-AT Production in a Multi-Stakeholder System

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    The e-NABLE community is a distributed collaborative volunteer effort to make upper-limb assistive technology devices available to end users. e-NABLE represents a do-it-yourself (DIY) approach to traditional prosthetic care. In order to learn about the attitudes and challenges of stakeholders working in and around e-NABLE, we conducted interviews with 12 volunteers in the e-NABLE movement and 3 clinicians. We found that volunteers derive a rich set of benefits from this form of altruistic activity; that both volunteers and clinicians recognize that end users benefit from aesthetic customization and personal choice in device selection; and that volunteers and clinicians bring separate, but potentially complementary, skills to bear on the processes of device provision. Based on these findings, we outline potential ways for volunteers and clinicians to optimize their talents and knowledge around the end goal of increased positive patient outcomes

    Seizure Detection, Seizure Prediction, and Closed-Loop Warning Systems in Epilepsy

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    Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy
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