Human motion detection using an earphone type wearable device and its application to health care （イヤホン型ウェアラブルデバイスを用いた身体動作検出とヘルスケアへの応用）

信州大学(Shinshu university)博士（工学）Thesis黒澤　真美. Human motion detection using an earphone type wearable device and its application to health care　（イヤホン型ウェアラブルデバイスを用いた身体動作検出とヘルスケアへの応用）. 信州大学, 2020, 博士論文. 博士（工学）, 甲第720号, 令和02年03月20日授与.doctoral thesi

An Ear Wearable Device System for Facial Emotion Recognition Disorders

A wearable device system was proposed in the present work to address the problem of facial emotion recognition disorders. The proposed system could comprehensively analyze the user’s own stress status, emotions of people around, and the surrounding environment. The system consists of a multi-dimensional physiological signals acquisition module, an image acquisition and transmission module, a user interface of the user mobile terminal, and a cloud database for data storage. Moreover, a deep learning based multi-model physiological signal pressure recognition algorithm and a facial emotion recognition algorithm were designed and implemented in the system. Some publicly available data sets were used to test the two algorithms, and the experiment results showed that the two algorithms could well realize the expected functions of the system

A Simple Method for Synchronising Multiple IMUs Using the Magnetometer

This paper presents a novel method to synchronise multiple IMU (inertial measurement units) devices using their onboard magnetometers. The method described uses an external electromagnetic pulse to create a known event measured by the magnetometer of multiple IMUs and in turn used to synchronise these devices. The method is applied to 4 IMU devices decreasing their de-synchronisation from 270ms when using only the RTC (real time clock) to 40ms over a 1 hour recording. It is proposed that this can be further improved to approximately 3ms by increasing the magnetometer’s sample frequency from 25Hz to 300Hz

Towards a Characterisation of Emotional Intent During Scripted Scenes Using In-ear Movement Sensors

Theatre provides a unique environment in which to obtain detailed data on social interactions in a controlled and repeatable manner.This work introduces a method for capturing and characterising the underlying emotional intent of performers in a scripted sceneusing in-ear accelerometers. Each scene is acted with different underlying emotional intentions using the theatrical technique ofActioning. The goal of the work is to uncover characteristics in the joint movement patterns that reveal information on the positive ornegative valence of these intentions. Preliminary findings over 3x12 (Covid-19 restricted) non-actor trials suggests people are moreenergetic and more in-sync when using positive versus negative intentions

Design Space and Usability of Earable Prototyping

Earable computing gains growing attention within research and becomes ubiquitous in society. However, there is an emerging need for prototyping devices as critical drivers of innovation. In our work, we reviewed the features of existing earable platforms. Based on 24 publications, we characterized the design space of earable prototyping. We used the open eSense platform (6-axis IMU, auditory I/O) to evaluate the problem-based learning usability of non-experts. We collected data from 79 undergraduate students who developed 39 projects. Our questionnaire-based results suggest that the platform creates interest in the subject matter and supports self-directed learning. The projects align with the research space, indicating ease of use, but lack contributions for more challenging topics. Additionally, many projects included games not present in current research. The average SUS score of the platform was 67.0. The majority of problems are technical issues (e.g., connecting, playing music)

Sensing with Earables: A Systematic Literature Review and Taxonomy of Phenomena

Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform

Sensing with Earables: A Systematic Literature Review and Taxonomy of Phenomena

Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform

Ethical problems of smart wearable devices

The stock market plays a major role in the entire financial market. How to obtain effective trading signals in the stock market is a topic that stock market has long been discussing. This paper first reviews the Deep Reinforcement Learning theory and model, validates the validity of the model through empirical data, and compares the benefits of the three classical Deep Reinforcement Learning models. From the perspective of the automated stock market investment transaction decision-making mechanism, Deep Reinforcement Learning model has made a useful reference for the construction of investor automation investment model, the construction of stock market investment strategy, the application of artificial intelligence in the field of financial investment and the improvement of investor strategy yield

Wearable Devices and their Implementation in Various Domains

Wearable technologies are networked devices that collect data, track activities and customize experiences to users? needs and desires. They are equipped, with microchips sensors and wireless communications. All are mounted into consumer electronics, accessories and clothes. They use sensors to measure temperature, humidity, motion, heartbeat and more. Wearables are embedded in various domains, such as healthcare, sports, agriculture and navigation systems. Each wearable device is equipped with sensors, network ports, data processor, camera and more. To allow monitoring and synchronizing multiple parameters, typical wearables have multi-sensor capabilities and are configurable for the application purpose. For the wearer?s convenience, wearables are lightweight, modest shape and multifunctional. Wearables perform the following tasks: sense, analyze, store, transmit and apply. The processing may occur on the wearer or at a remote location. For example, if dangerous gases are detected, the data are processed, and an alert is issued. It may be transmitted to a remote location for testing and the results can be communicated in real-time to the user. Each scenario requires personalized mobile information processing, which transforms the sensory data to information and then to knowledge that will be of value to the individual responding to the situation