Objective: evaluating stride events can be valuable for understanding the changes in walking due to aging and neurological diseases. However, creating the time series necessary for this analysis can be cumbersome. In particular, finding heel contact and toe-off events which define the gait cycles accurately are difficult. Method: we proposed a method to extract stride cycle events from tri-axial accelerometry signals. We validated our method via data collected from 14 healthy controls, 10 participants with Parkinson's disease, and 11 participants with peripheral neuropathy. All participants walked at self-selected comfortable and reduced speeds on a computer-controlled treadmill. Gait accelerometry signals were captured via a tri-axial accelerometer positioned over the L3 segment of the lumbar spine. Motion capture data were also collected and served as the comparison method. Results: our analysis of the accelerometry data showed that the proposed methodology was able to accurately extract heel and toe-contact events from both feet. We used t-tests, analysis of variance (ANOVA) and mixed models to summarize results and make comparisons. Mean gait cycle intervals were the same as those derived from motion capture, and cycle-to-cycle variability measures were within 1.5%. Subject group differences could be similarly identified using measures with the two methods. Conclusions: a simple tri-axial acceleromter accompanied by a signal processing algorithm can be used to capture stride events. Clinical impact: the proposed algorithm enables the assessment of stride events during treadmill walking, and is the first step toward the assessment of stride events using tri-axial accelerometers in real-life settings.

Filter banks are often used in the analysis and the synthesis of signals. By using cascade or parallel connected filters, multi-resolution analysis of a signal in different sub-bands is possible. A method based on the filter banks using discrete prolate spheroidal sequences (DPSS) is proposed in this paper. DPSS are solutions to an energy maximization problem in a limited bandwidth context; their time-frequency concentration aspect is exploited in the proposed design. A filter bank is derived using DPSS sequences and their modulated counter parts and is utilized in the analysis of non-stationary signals. The designed filter-bank achieves a concise representation of a non-stationary signal by maximizing the spectral energy in each sub-band.

Swallowing and swallowing disorders have garnered continuing interest over the past several decades. Electroencephalography (EEG) is an inexpensive and non-invasive procedure with very high temporal resolution which enables analysis of short and fast swallowing events, as well as an analysis of the organizational and behavioral aspects of cortical motor preparation, swallowing execution and swallowing regulation. EEG is a powerful technique which can be used alone or in combination with other techniques for monitoring swallowing, detection of swallowing motor imagery for diagnostic or biofeedback purposes, or to modulate and measure the effects of swallowing rehabilitation. This paper provides a review of the existing literature which has deployed EEG in the investigation of oropharyngeal swallowing, smell, taste and texture related to swallowing, cortical pre-motor activation in swallowing, and swallowing motor imagery detection. Furthermore, this paper provides a brief review of the different modalities of brain imaging techniques used to study swallowing brain activities, as well as the EEG components of interest for studies on swallowing and on swallowing motor imagery. Lastly, this paper provides directions for future swallowing investigations using EEG.