Related papers: Multimodal Earable Sensing for Human Energy Expend…
This paper presents a novel Electrodermal Activity (EDA) signal acquisition system, designed to address the challenges of stress monitoring in contemporary society, where stress affects one in four individuals. Our system focuses on…
Battery-powered wireless IoT devices are now widely seen in many critical applications. Given the limited battery capacity and inaccessibility to external power recharge, optimizing energy efficiency (EE) plays a vital role in prolonging…
The concern on human health is often overseen while wearable technologies attract exploding interests. Mainly due to the extreme proximity or a direct physical contact to the human skin, wearable communications devices are acknowledged to…
Step-counting has been widely implemented in wrist-worn devices and is accepted by end users as a quantitative indicator of everyday exercise. However, existing counting approach (mostly on wrist-worn setup) lacks robustness and thus…
Telehealth offers interesting avenues for improving healthcare access in vulnerable populations through use of electronic devices in the patient's home that monitor and assess for early complications. However, complication of operation and…
Earable acoustic sensing offers a powerful and non-invasive modality for capturing fine-grained auditory and physiological signals directly from the ear canal, enabling continuous and context-aware monitoring of cognitive states. As earable…
Wearables like smartwatches or wrist bands equipped with pervasive sensors enable us to monitor our physiological signals. In this study, we address the question whether they can help us to recognize our emotions in our everyday life for…
A limiting factor towards the wide routine use of wearables devices for continuous healthcare monitoring is their cumbersome and obtrusive nature. This is particularly true for electroencephalography (EEG) recordings, which require the…
Neurological disorders, including stroke, spinal cord injuries, multiple sclerosis, and Parkinson's disease, generally lead to diminished upper extremity (UE) function, impacting individuals' independence and quality of life. Traditional…
Electronic health monitoring is one of the major applications of wireless body area networks (WBANs) that helps with early detection of any abnormal physiological symptoms. In this paper, we propose and solve an optimization problem that…
eHealth systems deliver critical digital healthcare and wellness services for users by continuously monitoring physiological and contextual data. eHealth applications use multi-modal machine learning kernels to analyze data from different…
Advances in embedded devices and wireless sensor networks have resulted in new and inexpensive health care solutions. This paper describes the implementation and the evaluation of a wireless body sensor system that monitors human…
Earphones can give access to sensitive information via voice assistants which demands security methods that prevent unauthorized use. Therefore, we developed EarCapAuth, an authentication mechanism using 48 capacitive electrodes embedded…
Many promising applications of multimodal wearables require continuous sensing and heavy computation, yet users reject such devices due to privacy concerns. This paper shares our experiences building an ear-mounted voice-and-vision wearable…
Electronic Health Record (EHR) data encompass diverse modalities -- text, images, and medical codes -- that are vital for clinical decision-making. To process these complex data, multimodal AI (MAI) has emerged as a powerful approach for…
A wearable based long-term lifelogging system is desirable for the purpose of reviewing and improving users' lifestyle habits. Energy harvesting (EH) is a promising means for realizing sustainable lifelogging. However, present EH…
A substantial amount of growth is being achieved by businesses through IoT-based services. The emergent of small electronic devices capable of computing, which are commonly known as wearables in IoT domain has proven to have huge impact in…
Artificial intelligence (AI) models trained on audio data may have the potential to rapidly perform clinical tasks, enhancing medical decision-making and potentially improving outcomes through early detection. Existing technologies depend…
With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate (HR) detection systems. HR is a key physiological marker of cardiovascular health and physical fitness. Continuous…
This paper compares machine learning approaches with different input data formats for the classification of acoustic emission (AE) signals. AE signals are a promising monitoring technique in many structural health monitoring applications.…