Papers for
wearable device designers
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Random forest classifies real and imagined motor EEG signals accurately
Electroencephalography Signal Analysis for Human Activities Classification: A Solution Based on Machine Learning and Motor Imagery
Abstract: Electroencephalography (EEG) is a fundamental tool for understanding the brain's electrical activity related to human motor activities. Brain-Computer Interface (BCI) uses such electrical activity to develop assistive technologies, especially those directed at people with physical disabilities. However, extracting signal features and patterns is still complex, sometimes delegated to machine learning (ML) algorithms. Therefore, this work aims to develop a ML based on the Random Forest algorithm to classify EEG signals from subjects performing real and imagery motor activities. The interpretation and correct classification of EEG signals allow the development of tools controlled by cognitive processes. We evaluated our ML Random Forest algorithm using a consumer and a research-grade EEG system. Random Forest efficiently distinguishes imagery and real activities and defines the related body part, even with consumer-grade EEG. However, interpersonal variability of the EEG signals negatively affects the classification process.
Somatosensory activation supports focus during creative making process
Somatosensory Activation and Attentional States in Creative Making
Abstract: The methods for capturing the creative process come with associated tensions around memory recall, articulation, and communication during the act of making, as well as how to record these considerations. This paper has a twofold purpose: first, to offer an example of a mixed methodology, drawn from dance anthropology, sensory ethnography, and design, that applies embodied methods as an alternative for documenting creative making. Specifically, this incorporates the researcher-as-participant and the collation of fieldnotes, embodied knowledge/movement recall, with notation forms, and participant interviews. These are existing methods in dance anthropology; however, using them alongside exploratory prototyping and workshop approaches broadened this work into transdisciplinary practice. Second, it discusses the activation of somatosensory systems through wearable technology and the facilitation of heightened sensory awareness for the practitioner, leading to a subsequent ability to focus on creative decisions linked to reflection and metacognition.