Methods for Gastrointestinal Endoscopy Quantification: A Focus on Hands and Fingers Kinematics.

Gastrointestinal endoscopy is a complex procedure requiring the mastery of several competencies and skills. This procedure is in increasing demand, but there exist important management and ethical issues regarding the training of new endoscopists. Nowadays, this requires the direct involvement of real patients and a high chance of the endoscopists themselves suffering from musculoskeletal conditions. Colonoscopy quantification can be useful for improving these two issues. This paper reviews the literature regarding efforts to quantify gastrointestinal procedures and focuses on the capture of hand and finger kinematics. Current technologies to support the capture of data from hand and finger movements are analyzed and tested, considering smart gloves and vision-based solutions. Manus VR Prime II and Stretch Sense MoCap reveal the main problems with smart gloves related to the adaptation of the gloves to different hand sizes and comfortability. Regarding vision-based solutions, Vero Vicon cameras show the main problem in gastrointestinal procedure scenarios: occlusion. In both cases, calibration and data interoperability are also key issues that limit possible applications. In conclusion, new advances are needed to quantify hand and finger kinematics in an appropriate way to support further developments.

A Systematic Review of Commercial Smart Gloves: Current Status and Applications.

Smart gloves have been under development during the last 40 years to support human-computer interaction based on hand and finger movement. Despite the many devoted efforts and the multiple advances in related areas, these devices have not become mainstream yet. Nevertheless, during recent years, new devices with improved features have appeared, being used for research purposes too. This paper provides a review of current commercial smart gloves focusing on three main capabilities: (i) hand and finger pose estimation and motion tracking, (ii) kinesthetic feedback, and (iii) tactile feedback. For the first capability, a detailed reference model of the hand and finger basic movements (known as degrees of freedom) is proposed. Based on the PRISMA guidelines for systematic reviews for the period 2015-2021, 24 commercial smart gloves have been identified, while many others have been discarded because they did not meet the inclusion criteria: currently active commercial and fully portable smart gloves providing some of the three main capabilities for the whole hand. The paper reviews the technologies involved, main applications and it discusses about the current state of development. Reference models to support end users and researchers comparing and selecting the most appropriate devices are identified as a key need.

Collection and Processing of Data from Wrist Wearable Devices in Heterogeneous and Multiple-User Scenarios.

Over recent years, we have witnessed the development of mobile and wearable technologies to collect data from human vital signs and activities. Nowadays, wrist wearables including sensors (e.g., heart rate, accelerometer, pedometer) that provide valuable data are common in market. We are working on the analytic exploitation of this kind of data towards the support of learners and teachers in educational contexts. More precisely, sleep and stress indicators are defined to assist teachers and learners on the regulation of their activities. During this development, we have identified interoperability challenges related to the collection and processing of data from wearable devices. Different vendors adopt specific approaches about the way data can be collected from wearables into third-party systems. This hinders such developments as the one that we are carrying out. This paper contributes to identifying key interoperability issues in this kind of scenario and proposes guidelines to solve them. Taking into account these topics, this work is situated in the context of the standardization activities being carried out in the Internet of Things and Machine to Machine domains.

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students.

Wearable commercial-off-the-shelf (COTS) devices have become popular during the last years to monitor sports activities, primarily among young people. These devices include sensors to gather data on physiological signals such as heart rate, skin temperature or galvanic skin response. By applying data analytics techniques to these kinds of signals, it is possible to obtain estimations of higher-level aspects of human behavior. In the literature, there are several works describing the use of physiological data collected using clinical devices to obtain information on sleep patterns or stress. However, it is still an open question whether data captured using COTS wrist wearables is sufficient to characterize the learners' psychological state in educational settings. This paper discusses a protocol to evaluate stress estimation from data obtained using COTS wrist wearables. The protocol is carried out in two phases. The first stage consists of a controlled laboratory experiment, where a mobile app is used to induce different stress levels in a student by means of a relaxing video, a Stroop Color and Word test, a Paced Auditory Serial Addition test, and a hyperventilation test. The second phase is carried out in the classroom, where stress is analyzed while performing several academic activities, namely attending to theoretical lectures, doing exercises and other individual activities, and taking short tests and exams. In both cases, both quantitative data obtained from COTS wrist wearables and qualitative data gathered by means of questionnaires are considered. This protocol involves a simple and consistent method with a stress induction app and questionnaires, requiring a limited participation of support staff.