可穿戴设备发展现状及趋势

目前可穿戴设备尚没有标准的分类,文章从设备的物理形态、应用类型和通信方式对可穿戴设备进行划分,为可穿戴设备研究提供分类依据。并通过对驱动可穿戴设备的芯片、智能操作系统、电池和人机交互技术现状和发展分析,为可穿戴设备的发展提供预测依据。     

Operators in Process Industry: Interaction with Wearable Computers

Field operators need access to and interaction with more information than is available today during field operations. Wearable computers can provide relevant information with hands-free interaction techniques to support work tasks in the field. Potential implications of introducing wearable computers are collaboration, information overload, situation awareness and social relationships. Interaction techniques between humans are natural and well known and include voice, gestures, eye movements and biopotentials. Voice is a promising interaction technique for wearable computers, assuming improvements for rough and noisy environments. Wearable computers may change the roles and responsibilities while providing extended support for communication and collaboration. Field operators will be better prepared to prevent failures and secure quality. Control room operators will perform more administrative and management tasks. Correspondence and offprint requests to: C. Skourup, ABB AS, Corporate Research Center, Bergerveien 12, PO Box 90, N-1375 Billingstad, Norway. Email: charlotte.skourup@no.abb.com     

外骨骼机器人设计及其机械结构的有限元分析

为了提高士兵的承载能力,降低在运动过程中的能量损耗,设计了一种液压驱动的可穿戴外骨骼机器人,该外骨骼机器人下肢具有12个自由度。根据外骨骼机器人的功能和工作原理,分析了外骨骼机器人的关键部位,并设计了外骨骼机器人的各部分的尺寸。利用有限元软件ANSYS对外骨骼机器人进行建模,对下外骨骼机器人进行有限元静力学仿真,并对大腿部位进行瞬态动力学仿真。通过仿真研究分析,验证了机构设计的合理性,对外骨骼机器人的进一步研究提供了参考。     

Modelling the motion of a sensor attached to a walking person

This paper describes an implemented system to measure and track the motion of a sensor which is attached to a person or walking robot. The complex gait motion undergone by a structure-measuring sensor during walking introduces significant uncertainty into the measurements it takes. By measuring and tracking the sensor’s motion, more meaningful information can be obtained from the measurements, and predictions can be made about the future observed position of important objects in the world. The use of motion measuring devices is discussed and compared to the possible estimation of motion by the structural sensor itself. Analysis of walker motion is performed through the use of Gabor wavelets and the Extended Kalman Filter. The method tracks and predicts motions reliably while the walker walks in a straight path or turns corners. Particular attention is focussed on the case where the structural sensor is computer vision, and the benefit that a motion model can provide with it.     

Inhabiting the Body and the Spaces of Interaction

Wearable technologies create a tactile interface with the body of the user, providing a new model for architecture in which the surface between the subject and the object is almost seamless and sensory. As Valentina Croci explores, they also call on us to reconsider a more interdisciplinary mode of designing that accommodates a dynamic social dialogue between highly portable small-scale devices, their remote networks and the physical environment. Copyright © 2008 John Wiley & Sons, Ltd.     

There is more to context than location

Context is a key issue in interaction between human and computer, describing the surrounding facts that add meaning. In mobile computing location is usually used to approximate context and to implement context-aware applications. We propose that ultra-mobile computing, characterized by devices that are operational and operated while on the move (e.g. PDAs, mobile phones, wearable computers), can significantly benefit from a wider notion of context. To structure the field we introduce a working model for context, discuss mechanisms to acquire context beyond location, and application of context-awareness in ultra-mobile computing. We investigate the utility of sensors for context-awareness and present two prototypical implementations — a light-sensitive display and an orientation-aware PDA interface. The concept is then extended to a model for sensor fusion to enable more sophisticated context recognition. Based on an implementation of the model an experiment is described and the feasibility of the approach is demonstrated. Further, we explore fusion of sensors for acquisition of information on more sophisticated contexts.     

Enabling collaborative work: Work in progress

Mobile handheld and wearable computer systems to support fieldworkers (e.g. aircraft mechanics) doing a variety of tasks have been designed and studied by Carnegie Mellon University (CMU) researchers since 1991. We developed an initial research agenda and found some important results associated with the design and use of such systems. This article summarises some of the key findings from CMU's research and describes the results from one heuritic usability study about mobile handheld and wearable computer systems. Based on these research results, we put forward a new research agenda addressing important issues still to be resolved.     

Multi-sensor based real-time 6-DoF pose tracking for wearable augmented reality

Wearable augmented reality (WAR) combines a live view of a real scene with computer-generated graphic on resource-limited platforms. One of the crucial technologies for WAR is a real-time 6-DoF pose tracking, facilitating registration of virtual components within in a real scene. Generally, artificial markers are typically applied to provide pose tracking for WAR applications. However, these marker-based methods suffer from marker occlusions or large viewpoint changes. Thus, a multi-sensor based tracking approach is applied in this paper, and it can perform real-time 6-DoF pose tracking with real-time scale estimation for WAR on a consumer smartphone. By combining a wide-angle monocular camera and an inertial sensor, a more robust 6-DoF motion tracking is demonstrated with the mutual compensations of the heterogeneous sensors. Moreover, with the help of the depth sensor, the scale initialization of the monocular tracking is addressed, where the initial scale is propagated within the subsequent sensor-fusion process, alleviating the scale drift in traditional monocular tracking approaches. In addition, a sliding-window based Kalman filter framework is used to provide a low jitter pose tracking for WAR. Finally, experiments are carried out to demonstrate the feasibility and robustness of the proposed tracking method for WAR applications.     

Performance analysis of a DRL-less AFE for battery-powered wearable EEG

The driven-right-leg (DRL) circuit has been commonly used in the wall-powered EEG systems to reduce common-mode interference in the bio-potential amplifier. However, DRL circuit imposes limitations on the number of channels preventing modular development, and its effectiveness is diminished for a newer generation of battery-powered EEG systems. We present a performance investigation of DRL-less EEG circuit by designing a single-channel EEG with a novel Analog Front End (AFE) that contains a differential amplifier followed by a high-Q active notch filter. The prototyped wearable EEG system has been validated to record neural signals with and without the DRL circuit. The time domain and frequency domain signals show that the designed AFE is not impacted significantly (maximum 4 dB difference) by the DRL elimination and maintains similar signal quality. The customized EEG with and without DRL offers CMRR of 72.98 dB and 71.74 dB, respectively, at 60 Hz (power-line interference range in the USA), whereas CMRR of 72.64 dB and 71.01 dB, respectively, at 20 Hz (representative EEG signal range). DRL elimination allows us to envision a sensor-level modular EEG system for neural monitoring in non-clinical environments.     

Multi-class classification of construction hazards via cognitive states assessment using wearable EEG

Improving workers’ safety and health is one of the most critical issues in the construction industry. Research attempts have been made to better identify construction hazards on a jobsite by analyzing workers’ physical responses (e.g., stride and balance) or physiological responses (e.g., brain waves and heart rate) collected from the wearable devices. Among them, electroencephalogram (EEG) holds unique potential since it reveals abnormal patterns immediately when a hazard is perceived and recognized. Unfortunately, the unproven capacity of EEG signals for multi-hazard classification is a primary barrier towards ubiquitous hazard identification in real-time on jobsites. This study correlates EEG signal patterns with construction hazard types and develops an EEG classifier based on the experiments conducted in an immersive virtual reality (VR) environment. Hazards of different types (e.g., fall and slip/trip) were simulated in a VR environment. EEG signals were collected from subjects who wore both wearable EEG and VR devices during the experimentation. Two types of EEG features (time-domain/frequency-domain features and cognitive features) were extracted for training and testing. A total of eighteen advanced machine learning algorithms were used to develop the EEG classifier. The initial results showed that the LightGBM classifier achieved 70.1% accuracy based on the cognitive feature set for the 7-class classification. To improve the performance, the input data was relabeled, and three strategies were designed and tested. As a result, the combined approach (two-step ensemble classification) achieved 82.3% accuracy. As such, this study not only demonstrates the feasibility of coupling wearable EEG, VR, and machine learning to differentiate jobsite hazards but also provides strategies to improve multi-class classification performance. The research results support ubiquitous hazard identification and thereby contribute to the safety of the construction workplace.