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IEEE Transactions on Biomedical Engineering, November 2015

 

 

 

 

An Ear-Worn Vital Signs Monitor

David Da He, Eric S.‎ Winokur, and Charles G.‎ Sodini, Massachusetts Institute of Technology (MIT), USA, Volume 61, Issue 11, Pages: 2547-2552

This work presents a wearable device that can obtain key cardiovascular metrics at a single location.‎ The location is chosen to be the ear for its natural anchoring point and the availability of physiological signals.‎ The device measures the electrocardiogram (ECG), ballistocardiogram (BCG), and photoplethysmogram (PPG) to obtain the pre-ejection period (PEP), stroke volume, cardiac output, and pulse transit time (PTT).‎ Heart rate can be obtained from either the ECG, BCG, or PPG.‎ Combining the ECG and BCG allows for the estimation of the PEP.‎ Combining the BCG and PPG allows for the measurement of the PTT.‎ Additionally, the J-wave amplitude of the BCG is correlated to the stroke volume, which yields the cardiac output when combined with the heart rate.‎ Read More

Depth Sensing for Improved Control of Lower Limb Prostheses

Nili Krausz, Tommaso Lenzi, Levi Hargrove, Northwestern University and Rehabilitation Institute of Chicago, USA, Volume 61, Issue 11, Pages: 2576-2587 

 We developed, characterized, and validated an algorithm for recognizing stairs in the environment using data from a worn RGB-D sensor.‎ The measures that we extracted from the environment, including the distance to the stairs, angle of approach, height, width, and depth of stairs, and stair count, were characterized and found to be highly correlated and accurate.‎ Also, an estimate of when the user was approaching stairs was produced during an online walking test, which resulted in over 98% accuracy and a frame rate of more than 5 fps.‎ We plan to fuse the environmental estimates with information obtained from EMG, kinetics, and kinematics for predicting the correct locomotion mode for an ankle-knee prosthesis.‎ Read More

New Methods to Monitor Stair Ascents Using a Wearable Pendant Device Reveal How Behavior, Fear, and Frailty Influence Falls in Octogenarians

Matthew A.‎ Brodie, Kejia Wang, Kim Delbaere, Michela Persiani, Nigel H.‎ Lovell, Stephen J.‎ Redmond, Michael B.‎ Del Rosario, Stephen R.‎ Lord, Neuroscience Research Australia, University of New South Wales, Australia, Volume 62, Issue 11, Pages: 2595-2601

We investigated whether a freely worn pendant inertial sensor containing a triaxial accelerometer and a barometer could be used to accurately identify stair ascent by 52 older adults in free living.‎ Sensor-derived measures of stair ascent, comprising descriptors of intensity, variability and stability, were compared with prospective falls and a battery of clinical assessments comprising physiological, psychological, and health factors.‎ In healthy older people, fall related outcomes appeared more related to mental rather than physiological factors.‎ Stair ascent could be identified by scaling the barometer threshold to cadence, which may be useful in sensor-based remote monitoring of daily activity.‎ Read More

Heart Rate Detection during Sleep Using the Flexible RF Resonator and Injection-Locked PLL Sensor 

Sung Woo Kim, Soo Beom Choi, Yong-Jun An, Byung-Hyun Kim, Deok Won Kim, Jong-Gwan Yook, LG Electronics Advanced Research Institute, Yonsei University, Korea, Volume 61, Issue 11, Pages: 2568-2575

We investigated the effects of mechanical stiffness in parallel with the knee joint during walking in a series of experiments involving walking with a pair of knee exoskeletons with four levels of stiffness, including 0%, 33%, 66%, and 100% of the human knee quasi-stiffness.‎ We found that the ankle and hip joints retain relatively invariant moment and angle patterns.‎ The knee moment could fully adapt to the assistive moment;‎ whereas, the knee quasi-stiffness fully adapts to values of the assistive stiffness only up to ~80%.‎ Above this value we found biarticular consequences emerge at the hip joint.‎ Read More

Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG

Tim R.‎ Mullen, Christian A.‎ E.‎ Kothe, Yu Mike Chi,Alejandro Ojeda, Trevor Kerth, Scott Makeig, Tzyy-Ping Jung, Gert Cauwenberghs, Syntrogi Labs, Cognionics, Inc.‎, Kingston University, UK, University of California San Diego, USA Volume 61, Issue 11, Pages: 2553-2567 

In recent years, there have been significant advances in wearable, mobile, dry-electrode electroencephalography (EEG) systems.‎ These are yielding exciting new possibilities for scientific research, clinical diagnostics and therapeutics, and brain-computer interfaces (BCI) outside the clinic or laboratory.‎ However, these systems have been limited to a handful of channels mostly for applications of low-dimensional signal analysis in gaming and command control.‎ Here we describe and evaluate the first high-resolution dry mobile BCI system supporting real-time artifact rejection, imaging of distributed cortical network dynamics, and inference of cognitive state with a 64-channel dry-electrode wireless EEG headset.‎ Read More