Toward Evaluation of the Subjective Experience of a General Class of User-Controlled, Robot-Mediated Rehabilitation Technologies for Children with Neuromotor Disability

Abstract

Technological advances in game-mediated robotics provide an opportunity to engage children with cerebral palsy (CP) and other neuromotor disabilities in more frequent and intensive therapy by making personalized, programmed interventions available 24/7 in children’s homes. Though shown to be clinically effective and feasible to produce, little is known of the subjective factors impacting acceptance of what we term assistive/rehabilitative (A/R) gamebots by their target populations. This research describes the conceptualization phase of an effort to develop a valid and reliable instrument to guide the design of A/R gamebots. We conducted in-depth interviews with 8 children with CP and their families who had trialed an exemplar A/R gamebot, PedBotHome, for 28 days in their homes. The goal was to understand how existing theories and instruments were either appropriate or inappropriate for measuring the subjective experience of A/R gamebots. Key findings were the importance of differentiating the use case of therapy from that of assistance in rehabilitative technology assessment, the need to incorporate the differing perspectives of children with CP and those of their parents into A/R gamebot evaluation, and the potential conflict between the goals of preserving the quality of the experience of game play for the child while also optimizing the intensity and duration of therapy provided during play.

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

“…The model used the Arduino, connected by Bluetooth, to the notebook for processing and analysis of the signal by the MatLab software and Android smartphone. MatLab software was used in 24 works (IDs = 2, 3, 7, 8, 9, 11, 16,17,18,24,26,27,30,35,37,39,42,46,48,54,55,56,57,58).…”

“…The main tool used to pick up signals was the digital stethoscope, mentioning in 44 articles (IDs = 3, 4, 5, 6, 7, 8, 10,11,13,15,16,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,35,36,37,38,39,42,44,45,46,48,49,50,51,52,53,54,56). The connectivity of digital stethoscopes occurs, in most equipment, by Bluetooth, not over the Internet, and for this reason these equipments are not considered IoT devices.…”

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

“…Among the 58 articles selected, 45 works (77.59%, IDs = 3, 4, 5, 6, 7, 8, 10,11,13,15,16,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,35,36,37,38,39,42,41,44,45,46,48,49,50,51,52,53,54,56) cite heart rate observation methods. There are two widely used methods for observing the heartbeat.…”

“…The model used the Arduino, connected by Bluetooth, to the notebook for processing and analysis of the signal by the MatLab software and Android smartphone. MatLab software was used in 24 works (IDs = 2, 3, 7, 8, 9,11,16,17,18,24,26,27,30,35,37,39,42,46,48,54,55,56,57,58).…”

Machine Learning and IoT Applied to Cardiovascular Diseases Identification Through Heart Sounds: A Literature Review

“…Furthermore, the publication in recent years of a relevant number of literature reviews related to the application of ICT in health confirms the growing trend of research in this direction. These reviews addressed a variety of topics, such as gamification and serious games in the treatment of depression [16], use of robotics in human care [17,18], computing applied to education on noncommunicable chronic diseases [19], collection and analysis of physiological data in smart environments [20] and more recently Internet of Things (IoT) and occupational well-being in Industry 4.0 [21], application of machine learning on patient reported outcome measurements for predicting outcomes [22], and visual analytics for EHR [23].…”