Under some experimental task, the linear speed under consideration may have amplitudes ranging from near zero (no movement) to some value, say 50 cm/s. For instance, consider speed peaks frequently used as a feature of a parameter of interest. One may end up inferring and interpreting data that falls out of the actual range of values of the empirical data. The data of interest may even fail the range test under such pre-imposed (symmetric) theoretical assumptions ( Figure 1c). Statistical inferences from such theoretical treatment of the biophysical data are incongruent with the skewness found in empirical data ( Figure 1b). Consequently, the very information we are seeking to read out from the nervous systems can be lost. Under the Gaussian theoretical assumptions, these fluctuations are smoothed out through grand averages and treated as noise.
Often, common biophysical signals (e.g., from the amplitudes of speed, acceleration, heart rate, electroencephalography (EEG), electromyography (EMG)) reveal peaks and valleys that convey information about fluctuations in the activity of the nervous systems. Traditionally, several assumptions are made about the data that are not empirically informed. Part of the problem is the “one size fits all model” approach to the analyses ( Figure 1a). The deluge of data throughput that biosensors produce when continuously monitoring activities of the nervous systems over months and years can be challenging to analyze, interpret and decide upon.
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Yet how do we take full advantage of such time series data and produce verifiable and reproducible results that maximize objectivity and automate statistical inference? When paired with proper analytics, these biosensors’ activity can help us ascertain levels of volition and autonomy of the brain over the body in motion. The time series of signals they output can help us self-monitor the various levels of functionality of our nervous systems. In this sense, such “biosensors” have become ubiquitous in our lives and can be found embedded in smart devices and activity trackers that we commonly carry with us. Wearable sensors listening to the activities that are self-generated by the person’s nervous systems can help harness such biorhythmic activities in non-invasive ways. Along those lines, wearable sensing technologies are destined to help accelerate the transformation of medical and scientific practices, particularly in the areas of mental health. In the areas of mental health, this new platform is bound to revolutionize basic research and clinical practices, particularly if current subjective behavioral evaluations begin to be complemented with more objective and automated behavioral analyses. We are entering an era of Precision Medicine and mobile health, where personalized assessments at the clinic are combined with follow up assessments on the go. We further extend the use of our platform to characterize data from commercially available smart shoes, using gait patterns within a set of experiments that probe nervous systems functioning and levels of motor control. We report full distinction without confusion of the activities from the Kaggle set using a single parameter (linear acceleration or angular speed).
Here we use an open-access data set from to illustrate the use of a new statistical platform and standardized data types applied to smart phone accelerometer and gyroscope data from 30 participants, performing six different activities. Indeed, there is a critical need for fully objective automated analytical methods that easily handle the deluge of data these sensors output, while providing standardized scales amenable to apply across large sections of the population, to help promote personalized-mobile medicine. Further, there is no standardized scale or set of tasks amenable to take advantage of such technology in ways that permit broad dissemination and reproducibility of results. However, because most machine learning algorithms currently used to analyze such data require several steps that depend on human heuristics, the analyses become computationally expensive and rather subjective. For more information, go to LAR-15, LAR-8, LAR-6.8, LAR-458, LAR-9, LAR-40, LAR-47, LAR-300, LAR-15LH LEF-T, WYL-EHIDE, PRK-EHIDE, NSP, IRS, X-Series, and ROCK RIVER ARMS are registered trademarks of Rock River Arms, Inc.Wearable biosensors, such as those embedded in smart phones, can provide data to assess neuro-motor control in mobile settings, at homes, schools, workplaces and clinics. To cause cancer, birth defects or other reproductive harm. WARNING: The products offered for sale on this website can expose you to chemicals which are known to the State of California
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