Enhancing Clinical Supervision Using High-Definition Real-Time Digital Recording, Annotation, and Bluetooth Technology

2019 ◽  
Vol 4 (2) ◽  
pp. 356-362
Author(s):  
Jennifer W. Means ◽  
Casey McCaffrey

Purpose The use of real-time recording technology for clinical instruction allows student clinicians to more easily collect data, self-reflect, and move toward independence as supervisors continue to provide continuation of supportive methods. This article discusses how the use of high-definition real-time recording, Bluetooth technology, and embedded annotation may enhance the supervisory process. It also reports results of graduate students' perception of the benefits and satisfaction with the types of technology used. Method Survey data were collected from graduate students about their use and perceived benefits of advanced technology to support supervision during their 1st clinical experience. Results Survey results indicate that students found the use of their video recordings useful for self-evaluation, data collection, and therapy preparation. The students also perceived an increase in self-confidence through the use of the Bluetooth headsets as their supervisors could provide guidance and encouragement without interrupting the flow of their therapy sessions by entering the room to redirect them. Conclusions The use of video recording technology can provide opportunities for students to review: videos of prospective clients they will be treating, their treatment videos for self-assessment purposes, and for additional data collection. Bluetooth technology provides immediate communication between the clinical educator and the student. Students reported that the result of that communication can improve their self-confidence, perceived performance, and subsequent shift toward independence.

2017 ◽  
Vol 2 (11) ◽  
pp. 73-78
Author(s):  
David W. Rule ◽  
Lisa N. Kelchner

Telepractice technology allows greater access to speech-language pathology services around the world. These technologies extend beyond evaluation and treatment and are shown to be used effectively in clinical supervision including graduate students and clinical fellows. In fact, a clinical fellow from the United States completed the entire supervised clinical fellowship (CF) year internationally at a rural East African hospital, meeting all requirements for state and national certification by employing telesupervision technology. Thus, telesupervision has the potential to be successfully implemented to address a range of needs including supervisory shortages, health disparities worldwide, and access to services in rural areas where speech-language pathology services are not readily available. The telesupervision experience, potential advantages, implications, and possible limitations are discussed. A brief guide for clinical fellows pursuing telesupervision is also provided.


Author(s):  
Peter D. MacIntyre ◽  
Tammy Gregersen

Abstract The idiodynamic method is a relatively new mixed-method approach to studying in real time the complex dynamics of integrated affective and cognitive states that interact continuously with human communication. The method requires video recording a sample of communication from a research participant and then using specialized software to play the video back while collecting contemporaneous self-reported ratings (approximately one per second) on one or more focal variables of interest to the researcher, such as willingness to communicate (WTC) or communication anxiety (CA). After the participant rates the communication sample, a continuous graph of changes in the focal variable is printed. The final step is to interview the speaker to gather an explanation for changes in the ratings, for example at peaks or valleys in the graph. The method can also collect observer ratings that can then be compared with the speaker’s self-ratings. To date, studies have been conducted examining WTC, CA, motivation, perceived competence, teacher self-efficacy, teacher empathy, and strategy use, among other topics. The strengths and limitations of the method will be discussed and a specific example of its use in measuring WTC and CA will be provided.


2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.


2021 ◽  
pp. 1-17
Author(s):  
Shilin Peng ◽  
Xiao Jiang ◽  
Yongzhen Tang ◽  
Chong Li ◽  
Xiaodong Li ◽  
...  

Abstract Subglacial lake exploration is of great interest to the science community. RECoverable Autonomous Sonde (RECAS) provides an exploration tool to measure and sample subglacial lake environments while the subglacial lake remains isolated from the glacier surface and atmosphere. This paper presents an electronic control system design of 200 m prototype of RECAS. The proposed electronic control system consists of a surface system, a downhole control system, and a power transfer and communication system. The downhole control system is the core element of RECAS, and is responsible for sonde status monitoring, sonde motion control, subglacial water sampling and in situ analysis. A custom RS485 temperature sensor was developed to cater for the limited size and depth requirements of the system. We adopted a humidity-based measurement to monitor for a housing leak. This condition is because standard leak detection monitoring of water conductivity may be inapplicable to pure ice in Antarctica. A water sampler control board was designed to control the samplers and monitor the on/off state. A high-definition camera system with built-in storage and self-heating ability was designed to perform the video recording in the subglacial lake. The proposed electronic control system is proven effective after a series of tests.


2021 ◽  
pp. 1-11
Author(s):  
Tingting Zhao ◽  
Xiaoli Yi ◽  
Zhiyong Zeng ◽  
Tao Feng

YTNR (Yunnan Tongbiguan Nature Reserve) is located in the westernmost part of China’s tropical regions and is the only area in China with the tropical biota of the Irrawaddy River system. The reserve has abundant tropical flora and fauna resources. In order to realize the real-time detection of wild animals in this area, this paper proposes an improved YOLO (You only look once) network. The original YOLO model can achieve higher detection accuracy, but due to the complex model structure, it cannot achieve a faster detection speed on the CPU detection platform. Therefore, the lightweight network MobileNet is introduced to replace the backbone feature extraction network in YOLO, which realizes real-time detection on the CPU platform. In response to the difficulty in collecting wild animal image data, the research team deployed 50 high-definition cameras in the study area and conducted continuous observations for more than 1,000 hours. In the end, this research uses 1410 images of wildlife collected in the field and 1577 wildlife images from the internet to construct a research data set combined with the manual annotation of domain experts. At the same time, transfer learning is introduced to solve the problem of insufficient training data and the network is difficult to fit. The experimental results show that our model trained on a training set containing 2419 animal images has a mean average precision of 93.6% and an FPS (Frame Per Second) of 3.8 under the CPU. Compared with YOLO, the mean average precision is increased by 7.7%, and the FPS value is increased by 3.


2007 ◽  
Vol 6 (1) ◽  
pp. 89-103 ◽  
Author(s):  
Chris Weaver ◽  
David Fyfe ◽  
Anthony Robinson ◽  
Deryck Holdsworth ◽  
Donna Peuquet ◽  
...  

Understanding the spatial and temporal characteristics of individual and group behavior in social networks is a critical component of visual tools for intelligence analysis, emergency management, consumer analysis, and human geography. Identification and analysis of patterns of recurring events is an essential feature of such tools. In this paper, we describe an interactive visual tool for exploring the visitation patterns of guests at two hotels in central Pennsylvania from 1894 to 1900. The centerpiece of the tool is a wrapping spreadsheet technique, called reruns, that reveals regular and irregular periodic patterns of events in multiple overlapping artificial and natural calendars. Implemented as a coordinated multiple view visualization in Improvise, the tool is in ongoing development through an iterative process of data collection, transcription, hypothesis, design, discovery, analysis, and evaluation in close collaboration with historical geographers. Numerous discoveries have driven additional data collection from archival newspaper and census sources, as well as plans to enhance analysis of spatial patterns using historic weather records and railroad schedules. Distributed online evaluations of usability and usefulness have resulted in feature and design recommendations that are being incorporated into the tool.


Procedia CIRP ◽  
2016 ◽  
Vol 41 ◽  
pp. 920-926 ◽  
Author(s):  
Jonathan Downey ◽  
Denis O'Sullivan ◽  
Miroslaw Nejmen ◽  
Sebastian Bombinski ◽  
Paul O’Leary ◽  
...  

Author(s):  
Ryuta Yamaguchi ◽  
Panote Siriaraya ◽  
Da Li ◽  
Tomoki Yoshihisa ◽  
Shinji Shimojo ◽  
...  

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