Time domain analysis for foldable thin UWB monopole antenna

In this paper, the time domain analysis of an Ultra Wide Band antenna flexible circular monopole antenna is presented. The antenna is fabricated on liquid crystalline polymer flexible substrate with a compact geometry that makes it suitable for wearable applications under different bending conditions. The antenna is fed by coplanar waveguide transmission line and has a compact total size of 40 × 22 mm². Moreover, the antenna has good radiation efficiency (97%) over the bandwidth. The presented antenna has a good performance over the operating spectrum for straight and bending configurations. The design principals along with simulation and experimental results are presented in this contribution.     

Will smartwatches last? factors contributing to intention to keep using smart wearable technology

This study aims to deepen our understanding of the underlying factors affecting the intention to continue using increasingly popular wearable technology. A new theoretical model is developed and validated to extend traditional technology acceptance theories by identifying several value drivers of the continuous intention and actual usage of wearable devices. Hypotheses were tested using partial least squares path modeling on data collected from 383 actual smartwatch users. The results provide wearable device manufacturers with practical guidance for optimizing competition strategies. They also offer policy-making insights for practitioners to promote better wearable devices on the market, especially during the early stages of adoption.     

Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges

This paper firstly introduces common wearable sensors, smart wearable devices and the key application areas. Since multi-sensor is defined by the presence of more than one model or channel, e.g. visual, audio, environmental and physiological signals. Hence, the fusion methods of multi-modality and multi-location sensors are proposed. Despite it has been contributed several works reviewing the stateoftheart on information fusion or deep learning, all of them only tackled one aspect of the sensor fusion applications, which leads to a lack of comprehensive understanding about it. Therefore, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of the fusion methods of wearable sensors. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of multi-sensor applications for human activity recognition, including those recently added to the field for unsupervised learning and transfer learning. Finally, the open research issues that need further research and improvement are identified and discussed.     

Respiration-based emotion recognition with deep learning

Different physiological signals are of different origins and may describe different functions of the human body. This paper studied respiration (RSP) signals alone to figure out its ability in detecting psychological activity. A deep learning framework is proposed to extract and recognize emotional information of respiration. An arousal-valence theory helps recognize emotions by mapping emotions into a two-dimension space. The deep learning framework includes a sparse auto-encoder (SAE) to extract emotion-related features, and two logistic regression with one for arousal classification and the other for valence classification. For the development of this work an international database for emotion classification known as Dataset for Emotion Analysis using Physiological signals (DEAP) is adopted for model establishment. To further evaluate the proposed method on other people, after model establishment, we used the affection database established by Augsburg University in Germany. The accuracies for valence and arousal classification on DEAP are 73.06% and 80.78% respectively, and the mean accuracy on Augsburg dataset is 80.22%. This study demonstrates the potential to use respiration collected from wearable deices to recognize human emotions.     

基于柔性气压驱动器的可穿戴式腰部助力机器人研究

针对老龄化社会对于康复和看护助力搬运的需要,提出了用气压驱动器实现轻量、柔性助力、穿戴舒适的可穿戴式腰部助力机器人.机器人采用无外骨骼的结构设计,可以给护理人员在提升重物和静态保持作业时输出腰部所需助力,降低下腰痛（Low back pain,LBP）致病风险.通过对重物搬运作业中穿戴者竖脊肌表面肌电信号（Surface electromyography,sEMG）评估、基于测力平台最大搬举重量测试、静态弯腰负重作业下人体重心（Center of gravity,COG）移动轨迹等相关实验,验证了助力有效性.     

基于穿戴式加速度传感器的手势识别系统

采用ez430-chronos智能手表作为开发平台,利用其内置功能,读取手部运动产生的加速度数据,并将其传送至处理器。对目标手势采集一定量的数据,同时采集同样量的负样本—人手的自然动作,比较二者不同之处,确定目标手势的特征,并利用该特征识别目标手势。实验结果表明:该系统对目标手势有较高识别率,而对负样本有较高的误识别率,但可以通过设定更详细的识别特征进行改善。     

Energy‐Efficient Approximate Speech Signal Processing for Wearable Devices

As wearable devices are powered by batteries, they need to consume as little energy as possible. To address this challenge, in this article, we propose a synergistic technique for energy‐efficient approximate speech signal processing (ASSP) for wearable devices. More specifically, to enable the efficient trade‐off between energy consumption and sound quality, we synergistically integrate an approximate multiplier and a successive approximate register analog‐to‐digital converter using our enhanced conversion algorithm. The proposed ASSP technique provides ~40% lower energy consumption with ~5% higher sound quality than a traditional one that optimizes only the bit width of SSP.     

The warning glove – Development and evaluation of a multimodal action-specific warning prototype

This paper has two objectives: first, to introduce the concept of multimodal action-specific warnings and its prototypic realization in the form of a warning glove and second, to present the main findings of a user study that was conducted to test the warning glove against a conventional warning system. Regarding the first goal, the combination of multimodality and action-specificity was implemented by attaching electronic actuators on a right-handed glove for transmitting visual, auditory and tactile feedback. For the second objective, a user study was conducted to test the hypothesis that the warning glove is capable of obtaining faster responses and to determine the perceptions of the users regarding the appropriateness of the warning glove. The results confirmed the assumption of faster response times and participants perceived the warning glove to be ‘fairly appropriate’. These results warrant further development of this multimodal action-specific warning glove.