Movement Path Tortuosity in Free Ambulation: Relationships to Age and Brain Disease

Kearns, William D.; Fozard, James L.; Nams, Vilis O.

doi:10.1109/jbhi.2016.2517332

Cited by 22 publications

(23 citation statements)

References 45 publications

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“…Analysis of directional movement, referred to as tortuosity, is a common approach to study e.g. animal migration through the desert 29 and analysis of disease severity in cognitively impaired patients 30 , but has also been applied during cell tracking studies 31 . The tortuosity of movement indicates whether it is directional or random.…”

Section: Introductionmentioning

confidence: 99%

Quantification of wild-type and radiation attenuated Plasmodium falciparum sporozoite motility in human skin

Winkel

Korne

Oosterom

et al. 2019

Sci Rep

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Given the number of global malaria cases and deaths, the need for a vaccine against Plasmodium falciparum (Pf) remains pressing. Administration of live, radiation-attenuated Pf sporozoites can fully protect malaria-naïve individuals. Despite the fact that motility of these attenuated parasites is key to their infectivity and ultimately protective efficacy, sporozoite motility in human tissue (e.g. skin) remains wholly uncharacterized to date. We show that the ability to quantitatively address the complexity of sporozoite motility in human tissue provides an additional tool in the development of attenuated sporozoite vaccines. We imaged Pf movement in the skin of its natural host and compared wild-type and radiation-attenuated GFP-expressing Pf sporozoites. Using custom image analysis software and human skin explants we were able to quantitatively study their key motility features. This head-to-head comparison revealed that radiation attenuation impaired the capacity of sporozoites to vary their movement angle, velocity and direction, promoting less refined movement patterns. Understanding and overcoming these changes in motility will contribute to the development of an efficacious attenuated parasite malaria vaccine.

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Section: Introductionmentioning

confidence: 99%

Quantification of wild-type and radiation attenuated Plasmodium falciparum sporozoite motility in human skin

Winkel

Korne

Oosterom

et al. 2019

Sci Rep

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“…Meandering patterns of movement, reflected in a high fractal "D" score, identify cognitive impairment and separate demented from normal veterans. 50 This may also identify individuals with persistent symptoms following traumatic brain injury. 51 Combined with information from embedded neuropsychological testing, these technologies will provide additional insights into neurocognitive status.…”

Section: Neurophysiological Assessments Of Performance Readinessmentioning

confidence: 99%

Military applications of soldier physiological monitoring

Friedl

2018

Journal of Science and Medicine in Sport

135

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Wearable physiological status monitoring is part of modern precision medicine that permits predictions about an individual's health and performance from their real-time physiological status (RT-PSM) instead of relying on population-based predictions informed by estimated human, mission, and environmental/ambient conditions. RT-PSM systems have useful military applications if they are soldier-acceptable and provide important actionable information. Most commercially available systems do not address relevant military needs, typically lack the validated algorithms that make real time computed information useful, and are not open architected to be integrated with the soldier technological ecology. Military RT-PSM development requires committed investments in iterative efforts involving physiologists, biomedical engineers, and the soldier users. Military operational applications include: (1) technological enhancement of performance by providing individual status information to optimize self-regulation, workload distribution, and enhanced team sensing/situational awareness; (2) detection of impending soldier failure from stress load (physical, psychological, and environmental); (3) earliest possible detection of threat agent exposure that includes the "human sensor"; (4) casualty detection, triage, and early clinical management; (5) optimization of individual health and fitness readiness habits; and (6) long term health risk-associated exposure monitoring and dosimetry. This paper is focused on the performance-related applications and considers near term predictions such as thermal-work limits, alertness and fitness for duty status, musculoskeletal fatigue limits, neuropsychological status, and mission-specific physiological status. Each new measurement capability has provided insights into soldier physiology and advances the cycle of invention, lab and field testing, new discovery and redesign.

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“…Machine learning techniques 4 over data from wearable motion tracking devices based on inertial measurement units (IMUs) [5][6][7][8][9] are another major research line, including their use for medical purposes. 10 Diverse feature extraction methods have been proposed, such as the fractal dimension 11 for the characterization of walking path tortuosity of aging person, space-time representations of actions, 12 or the application of conditional random fields 13 for human motion recognition. Information fusion for human activity recognition [14][15][16] has targeted the combination of same kind sensors.…”

Section: Introductionmentioning

confidence: 99%

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals

Graña

Aguilar-Moreno

Asiaín

et al. 2020

Int. J. Neur. Syst.

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Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where the subjects can be carrying out natural activities while data recording is minimally invasive. Specifically, we aim to show that EEG and IMU data fusion allows improved human activity recognition in a natural setting. We have defined an experimental protocol composed of natural sitting, standing and walking activities, and we have recruited subjects in two sites: in-house ([Formula: see text]) and out-house ([Formula: see text]) populations with different demographics. Experimental protocol data capture was carried out with validated commercial systems. Classifier model training and validation were carried out with scikit-learn open source machine learning python package. EEG features consist of the amplitude of the standard EEG frequency bands. Inertial features were the instantaneous position of the body tracked points after a moving average smoothing to remove noise. We carry out three validation processes: a 10-fold cross-validation process per experimental protocol repetition, (b) the inference of the ethograms, and (c) the transfer learning from each experimental protocol repetition to the remaining repetitions. The in-house accuracy results were lower and much more variable than the out-house sessions results. In general, random forest was the best performing classifier model. Best cross-validation results, ethogram accuracy, and transfer learning were achieved from the fusion of EEG and IMUs data. Transfer learning behaved poorly compared to classification on the same protocol repetition, but it has accuracy still greater than 0.75 on average for the out-house data sessions. Transfer leaning accuracy among repetitions of the same subject was above 0.88 on average. Ethogram prediction accuracy was above 0.96 on average. Therefore, we conclude that wireless EEG and IMUs allow for the definition of natural experimental designs with high ecological validity toward human computational neuroethology research. The fusion of both EEG and IMUs signals improves activity and ethogram recognition.

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Movement Path Tortuosity in Free Ambulation: Relationships to Age and Brain Disease

Cited by 22 publications

References 45 publications

Quantification of wild-type and radiation attenuated Plasmodium falciparum sporozoite motility in human skin

Quantification of wild-type and radiation attenuated Plasmodium falciparum sporozoite motility in human skin

Military applications of soldier physiological monitoring

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals

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