Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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Annu Int Conf IEEE Eng Med Biol Soc · Jan 2015
Identifying stable phase coupling associated with cerebral autoregulation using the synchrosqueezed cross-wavelet transform and low oscillation morlet wavelets.
A novel method of identifying stable phase coupling behavior of two signals within the wavelet transform time-frequency plane is presented. The technique employs the cross-wavelet transform to provide a map of phase coupling followed by synchrosqueezing to collect the stable phase regime information. The resulting synchrosqueezed cross-wavelet transform method (Synchro-CrWT) is illustrated using a synthetic signal and then applied to the analysis of the relationship between biosignals used in the analysis of cerebral autoregulation function.
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Annu Int Conf IEEE Eng Med Biol Soc · Jan 2015
Heart Rate monitoring during physical exercise using wrist-type photoplethysmographic (PPG) signals.
Heart rate monitoring using wrist-type using photoplethysmographic (PPG) signals during subjects' intensive exercises is a challenging problem, since signals are strongly affected by motion artifacts caused by unexpected movements. This paper presents a method that uses both time and frequency characteristics of signals; using sparse signal reconstruction for high-resolution spectrum estimation. Experimental results on type data sets recorded from 12 subjects during fast running at peak speed of 15 km/hour. The results have a performance with the average absolute error being 1.80 beat per minute.
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Annu Int Conf IEEE Eng Med Biol Soc · Jan 2014
Electrically stimulated signals from a long-term Regenerative Peripheral Nerve Interface.
Despite modern technological advances, the most widely available prostheses provide little functional recovery beyond basic grasping. Although sophisticated upper extremity prostheses are available, optimal prosthetic interfaces which give patients high-fidelity control of these artificial limbs are limited. We have developed a novel Regenerative Peripheral Nerve Interface (RPNI), which consists of a unit of free muscle that has been neurotized by a transected peripheral nerve. ⋯ Signals exceeding 4 mV were successfully acquired and amplitudes were consistent across multiple repetitions of applied stimuli. There were no evident signs of muscle denervation, significant scar tissue, or muscle necrosis. This study provides further evidence that after a maturation period exceeding 1 year, reliable and consistent signals can still be acquired from an RPNI.
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Annu Int Conf IEEE Eng Med Biol Soc · Jan 2014
Clinical TrialThe use of inertial sensors for the classification of rehabilitation exercises.
The benefits of exercise in rehabilitation after orthopaedic surgery or following a musculoskeletal injury has been widely established. Within a hospital or clinical environment, adherence levels to rehabilitation exercise programs are high due to the supervision of the patient during the rehabilitation process. ⋯ The results show that a single sensor can accurately distinguish between seven commonly prescribed rehabilitation exercises with accuracies between 93% and 95%. Results also show that the use of multiple sensor units does not significantly improve results therefore suggesting that a single sensor unit can be used as an input to an exercise biofeedback system.
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Annu Int Conf IEEE Eng Med Biol Soc · Jan 2014
Clinical TrialA smartphone approach for the 2 and 6-minute walk test.
The 2 and 6-minute walk tests (2-6MWT) are used by rehabilitation professionals as a measure of exercise capacity. Our research has produced a new 2-6MWT BlackBerry smartphone application (app) that can be used to run the 2-6MWT and also provide new information about how the person moves during the test. The smartphone is worn on a belt at the lower back to record phone sensor data while walking. ⋯ The 2-6MWT app was evaluated in a pilot test using data from five able-bodied participants. Foot strike time was within 0.07 seconds when compared to gold standard video recordings. The total distance calculated by the app was within 1m of the measured distance.