人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.

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A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis

The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

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A review of wearable sensors and systems with application in rehabilitation.

Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context information about people actions. In this context, we describe in this work an Activity Recognition database, built from the recordings of 30 subjects doing Activities of Daily Living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repository. Results, obtained on the dataset by exploiting a multiclass Support Vector
Machine (SVM), are also acknowledged.

A public domain dataset for human activity recognition using smartphones

The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.

A tutorial on human activity recognition using body-worn inertial sensors

We perform continuous activity recognition using only two wrist-worn sensors - a 3-axis accelerometer and a microphone. We build on the intuitive notion that two very different sensors are unlikely to agree in classification of a false activity. By comparing imperfect, jumping window classifications from each of these sensors, we are able discern activities of interest from null or uninteresting activities. Where one sensor alone is unable to perform such partitioning, using comparison we are able to report good overall system performance of up to 70% accuracy. In presenting these results, we attempt to give a more-in depth visualization of the errors than can be gathered from confusion matrices alone.

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Gesture spotting using wrist worn microphone and 3-axis accelerometer

Conversation between two people involves subtle nonverbal coordination in addition to speech. However, the precise parameters and timing of this coordination remain unclear, which limits our ability to theorize about the neural and cognitive mechanisms of social coordination. In particular, it is unclear if conversation is dominated by synchronization (with no time lag), rapid and reactive mimicry (with lags under 1 s) or traditionally observed mimicry (with several seconds lag), each of which demands a different neural mechanism. Here we describe data from high-resolution motion capture of the head movements of pairs of participants (n = 31 dyads) engaged in structured conversations. In a pre-registered analysis pathway, we calculated the wavelet coherence of head motion within dyads as a measure of their nonverbal coordination and report two novel results. First, low-frequency coherence (0.2–1.1 Hz) is consistent with traditional observations of mimicry, and modeling shows this behavior is generated by a mechanism with a constant 600 ms lag between leader and follower. This is in line with rapid reactive (rather than predictive or memory-driven) models of mimicry behavior, and could be implemented in mirror neuron systems. Second, we find an unexpected pattern of lower-than-chance coherence between participants, or hypo-coherence, at high frequencies (2.6–6.5 Hz). Exploratory analyses show that this systematic decoupling is driven by fast nodding from the listening member of the dyad, and may be a newly identified social signal. These results provide a step towards the quantification of real-world human behavior in high resolution and provide new insights into the mechanisms of social coordination.

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Are You on My Wavelength? Interpersonal Coordination in Dyadic Conversations.

Evaluating the performance of a continuous activity recognition system can be a challenging problem To-date there is no widely accepted standard for dealing with this, and in general methods and measures are adapted from related fields such as speech and vision Much of the problem stems from the often imprecise and ambiguous nature of the real-world events that an activity recognition system has to deal with A recognised event might have variable duration, or be shifted in time from the corresponding real-world event Equally it might be broken up into smaller pieces, or joined together to form larger events Most evaluation attempts tend to smooth over these issues, using “fuzzy” boundaries, or some other parameter based error decision, so as to make possible the use of standard performance measures (such as insertions and deletions) However, we argue that reducing the various facets of a activity system into limited error categories – that were originally intended for different problem domains – can be overly restrictive In this paper we attempt to identify and characterise the errors typical to continuous activity recognition, and develop a method for quantifying them in an unambiguous manner

By way of an initial investigation, we apply the method to an example taken from previous work, and discuss the advantages that this provides over two of the most commonly used methods

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Evaluating performance in continuous context recognition using event-driven error characterisation

We introduce a method of using wrist-worn accelerometers to measure non-verbal social coordination within a group that includes autistic children. Our goal was to record and chart the children's social engagement - measured using interpersonal movement synchrony - as they took part in a theatrical workshop that was specifically designed to enhance their social skills. Interpersonal synchrony, an important factor of social engagement that is known to be impaired in autism, is calculated using a cross-wavelet similarity comparison between participants' movement data. We evaluate the feasibility of the approach over 3 live performances, each lasting 2 hours, using 6 actors and a total of 10 autistic children. We show that by visualising each child's engagement over the course of a performance, it is possible to highlight subtle moments of social coordination that might otherwise be lost when reviewing video footage alone. This is important because it points the way to a new method for people who work with autistic children to be able to monitor the development of those in their care, and to adapt their therapeutic activities accordingly.

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Sensing interpersonal synchrony between actors and autistic children in theatre using wrist-worn accelerometers

Human activity recognition (HAR) based on wearable sensors has emerged as an active topic of research in machine learning and human behavior analysis because of its applications in several fields, including health, security and surveillance, and remote monitoring. Machine learning algorithms are frequently applied in HAR systems to learn from labeled sensor data. The effectiveness of these algorithms generally relies on having access to lots of accurately labeled training data. But labeled data for HAR is hard to come by and is often heavily imbalanced in favor of one or other dominant classes, which in turn leads to poor recognition performance.In this study we introduce a generative adversarial network (GAN)-based approach for HAR that we use to automatically synthesize balanced and realistic sensor data. GANs are robust generative networks, typically used to create synthetic images that cannot be distinguished from real images. Here we explore and construct a model for generating several types of human activity sensor data using a Wasserstein GAN (WGAN). We assess the synthetic data using two commonly-used classifier models, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). We evaluate the quality and diversity of the synthetic data by training on synthetic data and testing on real sensor data, and vice versa. We then use synthetic sensor data to oversample the imbalanced training set. We demonstrate the efficacy of the proposed method on two publicly available human activity datasets, the Sussex-Huawei Locomotion (SHL) and Smoking Activity Dataset (SAD). We achieve improvements of using WGAN augmented training data over the imbalanced case, for both SHL (0.85 to 0.95 F1-score), and for SAD (0.70 to 0.77 F1-score) when using a CNN activity classifier.

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Jamie A. Ward

Papers

Gesture spotting using wrist worn microphone and 3-axis accelerometer

Are You on My Wavelength? Interpersonal Coordination in Dyadic Conversations.

Evaluating performance in continuous context recognition using event-driven error characterisation

Sensing interpersonal synchrony between actors and autistic children in theatre using wrist-worn accelerometers

Synthetic Sensor Data for Human Activity Recognition