Search for: All records

The popularity of applications involving physiological sensing (e.g., brain and muscle activity) and robotics has continued to grow in recent years. However, empirical studies evaluating ways to expose K-12 students to physiological computing are limited. To address this gap, we present PhysioBots, an educational tool designed to introduce K-12 students to physiological computing and robotics. We evaluated PhysioBots with 27 high school students between the ages of 15 and 17 to compare the use of physiological (e.g., self-induced changes in brain or muscle activity) and conventional control (e.g., keyboard) of a robot during a STEM education activity. Our preliminary results suggest that PhysioBots may improve students’ self-efficacy and programming confidence. Observations from open-ended survey questions also indicate that PhysioBots may support students in exploring ways to gamify emotional state manipulation. We discuss these findings and offer insights for future STEM education work involving physiological sensing and robotics. 

Artificial Intelligence and Machine Learning continue to increase in popularity. As a result, several new approaches to machine learning education have emerged in recent years. Many existing interactive techniques utilize text, image, and video data to engage students with machine learning. However, the use of physiological sensors for machine learning education activities is significantly unexplored. This paper presents findings from a study exploring students’ experiences learning basic machine learning concepts while using physiological sensors to control an interactive game. In particular, the sensors measured electrical activity generated from students’ arm muscles. Activities featuring physiological sensors produced similar outcomes when compared to exercises that leveraged image data. While students’ machine learning self-efficacy increased in both conditions, students seemed more curious about machine learning after working with the physiological sensor. These results suggest that PhysioML may provide learning support similar to traditional ML education approaches while engaging students with novel interactive physiological sensors. We discuss these findings and reflect on ways physiological sensors may be used to augment traditional data types during classroom activities focused on machine learning. 

It has been presumed that rheumatoid arthritis (RA) joint pain is related to inflammation in the synovium; however, recent studies reveal that pain scores in patients do not correlate with synovial inflammation. We developed a machine-learning approach (graph-based gene expression module identification or GbGMI) to identify an 815-gene expression module associated with pain in synovial biopsy samples from patients with established RA who had limited synovial inflammation at arthroplasty. We then validated this finding in an independent cohort of synovial biopsy samples from patients who had early untreated RA with little inflammation. Single-cell RNA sequencing analyses indicated that most of these 815 genes were most robustly expressed by lining layer synovial fibroblasts. Receptor-ligand interaction analysis predicted cross-talk between human lining layer fibroblasts and human dorsal root ganglion neurons expressing calcitonin gene–related peptide (CGRP⁺). Both RA synovial fibroblast culture supernatant and netrin-4, which is abundantly expressed by lining fibroblasts and was within the GbGMI-identified pain-associated gene module, increased the branching of pain-sensitive murine CGRP⁺dorsal root ganglion neurons in vitro. Imaging of solvent-cleared synovial tissue with little inflammation from humans with RA revealed CGRP⁺pain-sensing neurons encasing blood vessels growing into synovial hypertrophic papilla. Together, these findings support a model whereby synovial lining fibroblasts express genes associated with pain that enhance the growth of pain-sensing neurons into regions of synovial hypertrophy in RA.