Continuous, everyday ECG monitoring is essential for detecting transient heart conditions and enabling early intervention in cardiovascular diseases. However, current technologies, such as ECG Holter monitors and smartwatches, face challenges in balancing continuous monitoring with long-term wearability due to trade-offs in electrode placement. To address this, we present a novel ECG necklace that leverages its natural placement on the chest to provide continuous, clinically valuable ECG monitoring. Our design positions two electrodes on the left and right sides of the chest, approximating standard Lead I placement for accurate cardiac diagnostics. The necklace features an innovative skin moisture-enhanced electrode design for sustained comfort and integrates a compact 22-mm processing unit as the pendant, offering a 4-day battery life. In our studies, the ECG necklace demonstrated performance comparable to FDA-approved Holter monitors, with key features falling within a timing error range of 3.2–15.7 ms—well within acceptable limits. In our in-the-wild study, participants rated the necklace as highly comfortable and preferred it over traditional ECG monitors. As a widely accepted everyday accessory, the ECG necklace has the potential to seamlessly combine advanced functionality with daily wearability.
Heart rate is a key vital sign for cardiovascular health and fitness. However, the photoplethysmography (PPG) sensors that monitor heart rate in wearables struggle with accuracy during motion. Our day-long in-the-wild study shows Fitbit measures valid heart rates only 54.88% of the time. To address this, we developed PPG Earring, which measures 14 mm in diameter, weighs 2.0 g, and offers 21 hours of continuous sensing. Our eight-user exercise study shows that PPG Earring captures valid heart rate data for 91.74% of the time during exercise and 86.29% of our day-long in-the-wild study. All participants found the PPG Earring as comfortable as their regular earrings, and most participants expressed a strong willingness to wear the PPG Earring all the time every day. Our results validate the signal quality and comfort level of the PPG Earring, highlighting its potential as a daily health monitoring device.
This paper examines the potential of commercial earbuds for detecting physiological biomarkers like heart rate (HR) and heart rate variability (HRV) for stress assessment. Using accelerometer (IMU) and photoplethysmography (PPG) data from earbuds, we compared these estimates with reference electrocardiogram (ECG) data from 81 healthy participants. We explored using low-power accelerometer sensors for capturing ballistocardiography (BCG) signals. However, BCG signal quality can vary due to individual differences and body motion. Therefore, BCG data quality assessment is critical before extracting any meaningful biomarkers. To address this, we introduced the ECG-gated BCG heatmap, a new method for assessing BCG signal quality. We trained a Random Forest model to identify usable signals, achieving 82% test accuracy. Filtering out unusable signals improved HR/HRV estimation accuracy to levels comparable to PPG-based estimates. Our findings demonstrate the feasibility of accurate physiological monitoring with earbuds, advancing the development of user-friendly wearable health technologies for stress management.
This paper introduces PalateTouch, a hands-free earphone interaction system that leverages acoustic sensing technology to detect gestures resulting from the interaction between the tongue and the palate. By transmitting Zadoff-Chu signals and analyzing ear canal transfer function features, PalateTouch can capture subtle ear canal deformation and recognize various palate gestures used for interaction. Our proposed palate touch screening method ensures the system remains unaffected by unintended gestures from daily activities and the calibration mechanism enables our system to achieve user-independent recognition. Using only the earphone's built-in microphone and speaker, our system can distinguish nine gestures with an average F1 score of 0.92 and a false alarm rate of 0.02 across diverse conditions with 16 participants. Additionally, we have enabled real-time functionality and conducted a user study with 11 participants to evaluate PalateTouch's effectiveness in a demo application. The results demonstrate the superior performance and high usability of PalateTouch.
In-ear EEG research has traditionally treated biological signals other than brainwaves, such as electromyography (EMG) and electrooculography (EOG), as unwanted noise to be removed. However, instead of discarding these signals, we developed ID.EARS, a single-ear, dry electrode-based device that utilizes these signals for real-time gesture input. We first identified the optimal position for EEG measurement around the ear using the Alpha Attenuation Response (AAR) test and collected biological signals that occur alongside brainwaves at this location. Using these signals, we created a real-time artifact detection model capable of recognizing five specific gestures: blinking, left and right winking, teeth clenching, and chewing. This model achieved over 90% accuracy in cross-validation experiments. Leveraging this model and device, we propose several application scenarios, including music control, accessibility features, MR/XR control, and healthcare services. This innovative approach extends the use of ear-EEG devices beyond healthcare, opening up possibilities for natural user interfaces.
Understanding user input preferences is crucial in immersive environments, where input methods such as gestures and controllers are common. Traditional evaluation methods rely on post experience questionnaires, which don't capture real-time preferences. This study used brain signals to classify input preferences during Augmented Reality (AR) interactions. Thirty participants performed three interaction tasks (pointing, manipulation, and rotation) using hands or controllers. Their electroencephalogram (EEG) data were collected at varying task difficulties (low, medium, high) and phases (preparation, task, and completion). Machine learning was used to classify preferred and non-preferred input methods. Results showed that EEG signals effectively classify preferences with accuracies up to 86%, with the completion phase being the best indicator of preference. In addition, different input methods exhibited distinct EEG patterns. These findings highlight the potential of EEG signals for decoding real-time input preference in AR, offering insights for enhancing user experiences.
Flow, a state of deep task engagement, is associated with optimal experience and well-being, making its detection a prolific HCI research focus. While physiological sensors show promise for flow detection, most studies are lab-based. Furthermore, brain sensing during natural work remains unexplored due to the intrusive nature of traditional EEG setups. This study addresses this gap by using wearable, around-the-ear EEG sensors to observe flow during natural knowledge work, measuring EEG throughout an entire day. In a semi-controlled field experiment, participants engaged in academic writing or programming, with their natural flow experiences compared to those from a classic lab paradigm. Our results show that natural work tasks elicit more intense flow than artificial tasks, albeit with smaller experience contrasts. EEG results show a well-known quadratic relationship between theta power and flow across tasks, and a novel quadratic relationship between beta asymmetry and flow during complex, real-world tasks.