Robust heart rate estimation using wrist-based PPG signals in the presence of intense physical activities.

Heart rate tracking from a wrist-type photoplethysmogram (PPG) signal during intensive physical activities is a challenge that is attracting more attention thanks to the introduction of wrist-worn wearable computers. Commonly-used motion artifact rejection methods coupled with simple periodicity-based heart rate estimation techniques are incapable of achieving satisfactory heart rate tracking performance during intense activities. In this paper, we propose a two-stage solution.

Constructing energy expenditure regression model using heart rate with reduced training time.

Accurate estimation of energy expenditure (EE) is a key enabler for many applications of healthcare and wellness. Heart rate (HR) based EE estimation methods typically require extensive training time to establish a relationship between HR and EE. In this work, we propose a method where just the few most representative EE-HR data pairs are used to train the estimation model.

BioWatch: A Noninvasive Wrist-Based Blood Pressure Monitor That Incorporates Training Techniques for Posture and Subject Variability.

Noninvasive continuous blood pressure (BP) monitoring is not yet practically available for daily use. Challenges include making the system easily wearable, reducing noise level and improving accuracy. Variations in each person's physical characteristics, as well as the possibility of different postures, increase the complexity of continuous BP monitoring, especially outside the hospital.

BioWatch - a wrist watch based signal acquisition system for physiological signals including blood pressure.

A wrist watch based system, which can measure electrocardiogram (ECG) and photoplethysmogram (PPG), is presented in this work. By using both ECG and PPG we also measure pulse transit time (PTT), which studies show to correlate well with blood pressure (BP). The system is also capable of monitoring heart rate using either ECG or PPG and can monitor blood oxygenation by easily replacing the PPG sensors with a different set.

Maximizing information transfer rates in an SSVEP-based BCI using individualized Bayesian probability measures.

Successful brain-computer interfaces (BCIs) swiftly and accurately communicate the user's intention to a computer. Typically, information transfer rate (ITR) is used to measure the performance of a BCI. We propose a multi-step process to speed up detection and classification of the user's intent and maximize ITR.

A novel stimulation for multi-class SSVEP-based brain-computer interface using patterns of time-varying frequencies.

Steady-state visual evoked potential (SSVEP) has become one of the most widely employed modalities in online brain computer interface (BCI) because of its high signal-to-noise ratio. However, due to the limitations of brain physiology and the refresh rate of the display devices, the available stimulation frequencies that evoke strong SSVEPs are generally limited for practical applications. In this paper, we introduce a novel stimulation method using patterns of time-varying frequencies that can increase the number of visual stimuli with a fixed number of stimulation frequencies for use in multi-class SSVEP-based BCI systems.