This is so we can compare the results from different devices and accommodate the development of a general computational model. Ideally, the device should be provided with an open API that allows us to access the inputs. It’s vital that the signals are recorded with a defined unit of measurement. Access to raw data that can then be filtered and processed post-recording without losing any data from the original signal.The platform must be mobile, comfortable for the user, robust in regards to prolonged sensor contact, and have a sufficient battery-life to last throughout the day. The device is expected to collect data continuously without interfering with the user’s day-to-day tasks.However, we do not want this to compromise the fidelity of our results. These devices are low-cost to be accessible to almost anyone. So, what makes a device fit for the purpose of affective health research? They are affordable, mobile and provided with well-documented tools for research. These devices provide an accessible modality for data collection.
#GALVANIC SKIN RESPONSE WEARABLE PORTABLE#
The paper compares the following low-cost portable devices: What Technologies are Currently Available? This proposes a motivation to test how well they can imitate the quality of results acquired in lab experiments, supporting their potential in real-world applications. Recent advancements in affordable wearable devices allow us to monitor such physiological signals with reasonable accuracy. These signals can be used to validate semantic emotional descriptors based on valence and arousal measurements, linked to the user’s involuntary reactions transmitted by the Autonomic Nervous System (ANS).īITalino EDA/GSR sensor placed on the palm The paper above cites previous research to support their use of Heart Rate (HR) and Skin Conductance/Galvanic Skin Response (GSR) signals when designing a computational model for human emotion recognition. Our work concerns the detection of emotional responses with the use of personal devices and physiological sensors this closely relates to the aspects covered within the field of affective computing (AfC). A recent report, “ Towards the Development of Sensor Platform for Processing Physiological Data from Wearable Sensors” from the GEIST.re group at the AGH University of Science and Technology examines the use of low-cost portable devices currently available for researchers in the field. In our research, we focus on anxiety, depression and bipolar disorders.Ī critical area of our work considers methods for collecting and processing physiological data that can expose one’s affective patterns. As a team of researchers operating in the fields of Human-Computer Interaction (HCI), Digital Signal Processing, electrical engineering, neuroscience and clinical psychology, we’re driven to model common affective health disorders. Within the AffecTech project, we aim to better understand and regulate emotions with the use of embodied sensors and personal technologies.