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Background: Accurate and reliable prediction of changes in the severity of cerebellar ataxia (CA) will be necessary for trials of disease-modifying therapies. Cerebellar dysarthria (CD) is a common feature of CA. This study demonstrated that objective acoustic measures were more sensitive than perceptive analysis through the Scale for the Assessment and Rating of Ataxia (SARA) in assessing the progression of CD, within a time window of two years (mean). Method: Thirty-seven people with CA were tested at baseline (time point 1, TP1) and two years later (time point 2, TP2). A machine-learning framework with a robust three-step feature selection criterion and a Bayesian data-driven clustering technique based on the multivariate mixture extension of the generalized linear mixed model (GLMM) was used. The outcomes included two (time and cepstral-based) objective speech parameters recorded at TP1 and TP2. Subject testing involved dynamic prediction and was conducted using samples from the posterior distributions of parameter estimates and random effects. This study further employed the penalized expected deviance (PED) criterion for model comparison and the selection of the number of groups in the clustering procedure. Results: First, the selected objective speech metrics in the individual patients showed a significant worsening of the speech impairment (p $PED_{Fea1+Fea2}=5175$ ; number of groups = 2) performed more reliably than the univariate models ( $PED_{Fea1}=4225$ , $PED_{Fea2}=3850$ ; number of groups = 2) in discriminating the CA patients. Fourth, the individual-level predictions of the change in profiles of the objective measures over time were performed for the testing data. Conclusion: Such a framework using objective speech metrics indeed holds promise to predict the rate of clinical progression of CD in individuals with CA.

The progression of neurodegenerative conditions can be effectively monitored and improved by using objective assessments. The conditions such as Friedreich Ataxia (FA) are clinically assessed by means of subjective measures commonly practised in clinics. Here, we propose a device capable of measuring ataxia, in the form of a `cup' capable of sensing certain kinematic parameters of interest while engaging in an activity that is closely related to daily living. In this study, the functional task of 'drinking' was utilised to diagnose participants with FA and capture features in terms of diagnosis (separation) and correlation with the clinical scales. Frequency domain analysis was incorporated enabling the classification of control subjects and FA patients to an accuracy of 88% with a correlation of 90% with the clinical scores.

Cerebellar ataxia (CA) refers to the impaired balance and coordination resulting from injury or degeneration of the cerebellum. Testing balance is one of the simplest means of assessing CA. This study compares instrumented assessment and clinical assessment scales of the balance test called Romberg's test. Inertial Measurement Unit (IMU) data were collected from a sensor attached to their chest of 53 subjects while they performed the test. The corresponding clinical scores were also tabulated. Using this data, 99 features were extracted to quantify acceleration, tremor and displacement of body sway. These features were filtered to identify the subset that better characterize the distinctive behavior of CA subjects. Elastic Net Regression model resulted a greater agreement (0.70 Pearson coefficient) with the clinical SARA scores. The overall results indicated that data from a single IMU sensor is sufficient to accurately assess balance in CA. The significance of this study is that evaluation of balance using Recurrence Quantification Analysis produces a comprehensive framework for the assessment of CA.

Friedreich ataxia (FRDA), the most common of the inherited ataxias, is a degenerative disease that progressively affects walking and other functions leading to significant impairment associated with a shortened lifespan. It is important to monitor the progression of ataxia over periods of time for clinical and therapeutic interventions. This study was aimed at investigating the use of our instrumented measurement scheme of utilizing a motion detecting spoon in a self-feeding activity to quantify the longitudinal effect of FRDA on upper limb function. Forty individuals diagnosed with FRDA (32.8±14.9 years old) were recruited in a 12-month longitudinal study consisting of equal number of males and females (20). A set of biomarkers was extracted from the temporal and texture analysis of the movement time series data that objectively detected subtle changes during follow-up testing. The results indicated that both analyses generated features that resembled clinical ratings. Although the diagnosis and severity related performances were readily observed by temporal features, the longitudinal progression was better captured by the textural features (p = 0.029). The estimation of severity by mean of random forest regression model and LASSO exhibited a high degree of parity with the standard clinical scale (rho = 0.73, p < 0.001).

The importance of First Year Engineering Student (FYES) retention and overall success is predicated on their recent academic success in high school to an often struggling first semester student. Many FYES have limited knowledge of how a mathematical equation relates to a physical concept, for example the sine wave function meaning as a sound wave or harmonic oscillator. A FYES typically takes Pre-Calculus or Calculus where functions are presented including the sine wave function. Through practical lecture material of wave motion and hands-on problem solving using Matlab the FYES student learning is enhanced. The purpose of this paper is to describe the teaching method used for explaining wave motion using Matlab and everyday-life analogies along with lessons learned.

A hallmark of cerebellar disease is impaired accuracy of intended movement which is often summarized as ataxia or incoordination. The diagnosis and assessment of cerebellar ataxia (CA) is primarily based on the expert clinician's visual and auditory observations of the performance of these tasks, and as such, a significant level of subjectivity is implied. In order to address the limitations of this subjectivity we designed a novel automated system, utilizing the Microsoft Kinect device, to capture the finger chase task (in the assessment of upper limb ataxia) which is a part of the assessment of cerebellar upper limb function. Capturing the movements of the marker attached on the subject's finger when following the target point generated by the program that mimics the finger movement of the clinician, we were able to capture the disability and provide a novel objective measure of the CA affecting upper limb function. In our approach, we essentially quantified the difference between the intended and achieved trajectories using Dynamic Time Warping (DTW) technique. Further, signal delay times and directional changes of the velocity of the marker were considered in characterizing the disability associated with patient's finger movements. Finally, Principal Component Analysis (PCA) was employed to combine all the relevant features, reduce feature dimension while enhancing the robustness. This analysis demonstrates a significant separation between normal subjects and CA patients, highlighting this approach as a potential diagnostic aid in the objective assessment of Cerebellar ataxia.

The aim of this research was to analyse the characteristic movements of patients with Parkinsons disease (PD) during the pull test. In this experiment, flexibility of participants were measured from two wearable sensors attached on their upper and lower back. In particular, as Bradykinesia and axial Bradykinesia are vital characteristics which are challenging to measure, we designed a test system engaging a minimal number of wearable sensors to capture the characteristic movements of the back. We utilised a time delay between two sensors to analyse rigidity of human back. In order to measure the characteristics of patient and control groups, the principal component analysis (PCA) was applied to extract the significant features to distinguish the two groups. Consequently, their differences were shown in PCA with a satisfactory separation of controls and patients.

Quantitative identification of dysarthria plays a major role in the classification of its severity. This paper quantitatively analyses several components of cerebellar dysarthria. The methodology described in this study will be extended to other types of dysarthria via systematic analysis. The speech production model is characterized as a second-order singleinput and single-output (SISO), linear, time-invariant (LTI) system in our study. A comparative study on the behavior of the damping ratio and resonant frequency for dysarthric and non-dysarthric subjects is presented. The results are further analyzed using the Principal component analysis (PCA) technique to emphasize the variation and uncover strong patterns in the selected features. The effects of some other related factors like decay time and Q-factor are also highlighted.

There is still much to learn about ocean ecosystems. Long-term, continuous observations of underwater environments can help improve our understanding. This is especially important as we continue to develop ocean energy resources. The Pacific Northwest National Laboratory (PNNL) and the University of Washington have developed a real-time processing system for sonar to detect and track animals, and to extract water column biomass statistics in order to facilitate continuous monitoring of an underwater environment. The Nekton Interaction Monitoring System (NIMS) is built to continuously process in real-time a stream of sonar data and archive tracking and biomass data, reducing the volume of stored data by orders of magnitude. The prototype system has been tested for real-time performance in the laboratory and in an operational environment on-board a research vessel and as part of a multi-instrument monitoring system. NIMS currently works with three types of sonar: a multi-beam sonar, a split-beam echo sounder and an acoustic camera.

The vision of the US Integrated Ocean Observing System (U.S. IOOS) is to provide information and services to the nation for enhancing our understanding of the ecosystem and climate; sustaining living marine resources; improving public health and safety; reducing impacts of natural hazards and environmental changes; and expanding support for marine commerce and transportation. In the last decade U.S. IOOS has made considerable progress in advancing physical and chemical observing systems, but only modest progress integrating biological observations from disparate data providers, and there remain challenges to fully integrate biological observing systems into U.S. IOOS. Recent technological advances in miniature, low power “bio” sensor can record everything from plankton greater than 20 micrometer with electro-optical technology, to hydroacoustic sensors that can record meso-zooplankton and nekton from mobile autonomous platforms, to satellite linked recorders that can record the movement and behavior of the largest marine predators. This opens up remarkable opportunities for observing the biotic realm at critical spatio-temporal scales that are most relevant to organisms, which have been out of reach until present. “bio”sensor technology is mature and proven to be operational and biological monitoring should be an integrated component of observing systems. Optimally, it should be clearly defined and implemented in close association with physical oceanographers. The integration of biological observing into U.S IOOS will only strengthen the national observing capabilities to respond to the growing needs for ecosystem observation to support ecosystem-based approaches and sustain our living marine resources.

DEIMOS (Deepwater Echo Integrating Marine Observatory System) is an active acoustic package deployed at the MARS cabled observatory in Monterey Bay, CA. Located on the continental slope at a depth of 875 m, DEIMOS transmits an acoustic pulse upwards every five seconds, recording the intensity of reflected sound (i.e. backscatter) from organisms in the water column. Designed as a robust, ROV-deployable package, it is built around a Simrad EK60 38 kHz scientific echosounder. The 7°, split-beam transducer is oil-filled to withstand pressure at depth. Electronics, including the general-purpose transceiver, an addressable web relay for remote rebooting, and a power filter and step-down, are housed in a 17-inch Teledyne Benthos borosilicate glass pressure sphere. The entire assembly is bolted to a galvanized steel frame and is attached to the MARS node by a 50-m Falmat Ethernet cable with an ODI wetmate connector. DEIMOS was deployed in February 2009 by the ROV Ventana from the R/V Point Lobos. Supplied with power and a communications link from the MARS node, DEIMOS is controlled remotely, and yields near-real-time backscatter data from the seabed to the surface. Since deployment, we have implemented automated data handling to monitor data acquisition, issue email alerts if it stops, and to upload data from the acquisition servers at Monterey Bay Aquarium Research Institute (MBARI) to the Fisheries Acoustics Research Lab in Seattle. Monterey Bay is located in the eastern-boundary California Current. Its food web depends on nutrients supplied by episodic, seasonal upwelling events, and its pelagic fauna are dominated by invertebrates. A variety of biological patterns are visible in the acoustic record. Several backscattering layers undergo diel vertical migrations, and change in number, depth, and density with the seasons. Episodic events such as high-density aggregations and large surface-diving targets are also present. We are developing metrics to characterize temporally-indexed, vertical density distributions. Total backscatter density increased from late spring to early autumn, while simultaneously becoming more dispersed and moving deeper in the water column. These changes reflect a decrease in the number of vertically migrating layers, and the appearance of a large, deep, non-migratory layer centered near 500 m depth. Late winter and spring contained short-term variability, due to the presence of near-surface aggregations. Long-term deployments of stationary acoustic instruments provide high resolution data over a range of temporal scales that is rare for aquatic biological research.

The J-QUEST instrument package, a quantitative 70 kHz split-beam echo sounder and a stereo TV camera, is being developed to observe mesopelagic fish. In situ acoustic and optical measurements of single fish within aggregations include swimming speed, target strength, length, and tilt angle. Acoustic data and two video signals are monitored in real-time via an optical-power composite cable and receivers on deck. The J-QUEST was deployed from the R/V Kaiyo-maru in the transient waters of the Kuroshio current from Kashima-nada to off the coast of Sanriku in the east of Japan. J-QUEST was tethered from the surface to a depth of 200 m and worked well. In Kashima-nada at a depth of 10 m, Pacific saury (Cololabis saira) were attracted to the underwater lights and formed a large aggregation around J-QUEST. We could monitor fish on both the TV monitors and on the echogram. Average target strength of Pacific saury was -37.9 dB and normalized TS (by squared fork length) was -66.8 dB