A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain human dynamics or behaviors and then use them as a way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows a nested structure to be built to summarize data at multiple levels. We demonstrate our framework on five datasets where the advantages of the proposed approach are validated

Abnormal brain state distribution and network connectivity in a SYNGAP1 rat model

Mutations in the SYNGAP1 gene are one of the common predictors of neurodevelopmental disorders, commonly resulting in individuals developing autism, intellectual disability, epilepsy, and sleep deficits. EEG recordings in neurodevelopmental disorders show potential to identify clinically translatable biomarkers to both diagnose and track the progress of novel therapeutic strategies, as well as providing insight into underlying pathological mechanisms. In a rat model of SYNGAP1 haploinsufficiency in which the exons encoding the calcium/lipid binding and GTPase-activating protein domains have been deleted (Syngap(+/Δ−GAP)), we analysed the duration and occurrence of wake, non-rapid eye movement and rapid eye movement brain states during 6 h multi-electrode EEG recordings. We find that although Syngap(+/Δ−GAP) animals spend an equivalent percent time in wake and sleep states, they have an abnormal brain state distribution as the number of wake and non-rapid eye movement bouts are reduced and there is an increase in the average duration of both wake and non-rapid eye movement epochs. We perform connectivity analysis by calculating the average imaginary coherence between electrode pairs at varying distance thresholds during these states. In group averages from pairs of electrodes at short distances from each other, a clear reduction in connectivity during non-rapid eye movement is present between 11.5 Hz and 29.5 Hz, a frequency range that overlaps with sleep spindles, oscillatory phenomena thought to be important for normal brain function and memory consolidation. Sleep abnormalities were mostly uncorrelated to the electrophysiological signature of absence seizures, spike and wave discharges, as was the imaginary coherence deficit. Sleep spindles occurrence, amplitude, power and spread across multiple electrodes were not reduced in Syngap(+/Δ−GAP) rats, with only a small decrease in duration detected. Nonetheless, by analysing the dynamic imaginary coherence during sleep spindles, we found a reduction in high-connectivity instances between short-distance electrode pairs. Finally comparing the dynamic imaginary coherence during sleep spindles between individual electrode pairs, we identified a group of channels over the right somatosensory, association and visual cortices that have a significant reduction in connectivity during sleep spindles in mutant animals. This matched a significant reduction in connectivity during spindles when averaged regional comparisons were made. These data suggest that Syngap(+/Δ−GAP) rats have altered brain state dynamics and EEG connectivity, which may have clinical relevance for SYNGAP1 haploinsufficiency in humans

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms

Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices

Pedometer step counting in South Africa: tools or trinkets?

Objectives. This study addressed (i) the accuracy of measuring ambulatory signals and (ii) the susceptibility to nonambulatory signals, of the Discovery Vitality Pedometer (VT) and the Kellogg's Special K Step Counter (KL) compared with three research-grade pedometers (DW: Yamax DigiWalker SW-401, MTI: MTI Actigraph AM-7164-2.2 , NL: New Lifestyles NL 2000). Design. One hundred instruments (20 instruments/brand) were tested at five level walking speeds on a motorised treadmill (3.24, 4.02, 4.80, 5.64, 6.42 km.hr-1) and during motor vehicle travel on tarred roads (62.9 km). Results. The KL was highly variable across all speeds, while the VT tended to be variable at the lowest speed. The DW, NL and VT significantly underestimated steps below 4.80km.hr-1 (41 - 94%, p < 0.02) but accuracy improved at speeds &#8805; 4.80 km.hr-1 (98 - 102%). The KL displayed the highest variability (60% inter-instrument variance) followed by the VT (10% inter-instrument variance). The research-grade pedometers were the least variable (0 - 1% inter-instrument variance). At 4.80 km.hr-1, all research- grade pedometers measured within a 10% margin of error compared with the 90% of VT units and 42% of KL units. The VT was significantly more resistant to nonambulatory signals than the DW (p < 0.01). The KL was the most variable in its response to non-ambulatory signals while the NL was the most consistent. The MTI detected the most non-ambulatory signals (p < 0.05). Conclusions. The KL should not be used as a promotional pedometer. The VT achieved the minimum standards required of a promotional pedometer. Further testing is required for longevity, and performance under free-living conditions. South African Journal of Sports Medicine Vol. 18 (3) 2006: pp. 67-7

Behavioral responses of male \u3ci\u3eDiaphorina citri\u3c/i\u3e (Hemiptera: Liviidae) to mating communication signals from vibration traps in citrus (Sapindales: Rutaceae) trees

The Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Liviidae), vectors the bacterium causing citrus greening disease, which has devastated citrus production worldwide wherever it has been introduced. To help monitor and target D. citri populations in commercial groves, thereby facilitating more effective management of citrus greening disease, a prototype device has been developed that mimics D. citri female vibrational communication signals, attracting males to a trap. For this report, effects of the device on male D. citri searching behavior were assessed to consider potential improvements in field applications. Forty-five percent of the males that searched towards the female signal mimic reached the source. In addition, the mean latencies before the initiation of calling and searching responses by males that reached the source were significantly lower than for those that missed, which suggests that trapping efficiency is strongly influenced by variability in male responsiveness to searching cues. Consequently, it is likely that the trapping efficiency of vibration traps could be increased further if they were modified to make use of additional cues strongly attractive to males, such as citrus flush olfactory and visual cues. El sílido asiático de los cítricos Diaphorina citri Kuwayama (Hemiptera: Liviidae) es un vector de la bacteria causante de la enfermedad del enverdecimiento de los cítricos, que ha devastado la producción de cítricos en todo el mundo dondequiera que este ha sido introducido. Para ayudar a monitorear y enfocarse a las poblaciones de D. citri en los bosques comerciales, facilitando así un manejo más efectivo de la enfermedad del enverdecimiento de los cítricos, se ha desarrollado un aparato prototipo que imita las señales vibracionales de comunicación de las hembras de D. citri, que atraen a machos a las trampas. Para este informe, se evaluaron los efectos del aparato sobre el comportamiento de búsqueda de los machos de D. citri para considerar posibles mejoras en las aplicaciones de campo. El 45% de los machos que buscaban hacia la señal que imitaba la hembra llegaron a la fuente. Además, el promedio de la latencia antes de la iniciación de la llamada y las respuestas de búsqueda por los machos que llegaron a la fuente fueron significativamente más bajos que para los que se fallaron, lo que sugiere que la eficiencia de captura está fuertemente influenciada por la variabilidad en la capacidad de respuesta de los machos a buscar señales. En consecuencia, es probable que la eficacia de captura de trampas de vibración podría aumentar aún más si se modificaron para hacer uso de señales adicionales fuertemente atractivas para los machos, tales como señales olfativas y visuales de los brotes de nuevas hojas en cítricos

Modelling Patient Behaviour Using IoT Sensor Data: a Case Study to Evaluate Techniques for Modelling Domestic Behaviour in Recovery from Total Hip Replacement Surgery

The UK health service sees around 160,000 total hip or knee replacements every year and this number is expected to rise with an ageing population. Expectations of surgical outcomes are changing alongside demographic trends, whilst aftercare may be fractured as a result of resource limitations. Conventional assessments of health outcomes must evolve to keep up with these changing trends. Health outcomes may be assessed largely by self-report using Patient Reported Outcome Measures (PROMs), such as the Oxford Hip or Oxford Knee Score, in the months up to and following surgery. Though widely used, many PROMs have methodological limitations and there is debate about how to interpret results and definitions of clinically meaningful change. With the development of a home-monitoring system, there is opportunity to characterise the relationship between PROMs and behaviour in a natural setting and to develop methods of passive monitoring of outcome and recovery after surgery. In this paper, we discuss the motivation and technology used in long-term continuous observation of movement, sleep and domestic routine for healthcare applications, such as the HEmiSPHERE project for hip and knee replacement patients. In this case study, we evaluate trends evident in data of two patients, collected over a 3-month observation period post-surgery, by comparison with scores from PROMs for sleep and movement quality, and by comparison with a third control home. We find that accelerometer and indoor localisation data correctly highlight long-term trends in sleep and movement quality and can be used to predict sleep and wake times and measure sleep and wake routine variance over time, whilst indoor localisation provides context for the domestic routine and mobility of the patient. Finally, we discuss a visual method of sharing findings with healthcare professionals

Classification of Different Shoulder Girdle Motions for Prosthesis Control Using a Time-Domain Feature Extraction Technique

Abstract—The upper limb amputation exerts a significant burden on the amputee, limiting their ability to perform everyday activities, and degrading their quality of life. Amputee patients’ quality of life can be improved if they have natural control over their prosthetic hands. Among the biological signals, most commonly used to predict upper limb motor intentions, surface electromyography (sEMG), and axial acceleration sensor signals are essential components of shoulder-level upper limb prosthetic hand control systems. In this work, a pattern recognition system is proposed to create a plan for categorizing high-level upper limb prostheses in seven various types of shoulder girdle motions. Thus, combining seven feature groups, which are root mean square, four-order autoregressive, wavelength, slope sign change, zero crossing (ZC), mean absolute value, and cardinality. In this article, the time-domain features were first extracted from the EMG and acceleration signals. Then, the spectral regression (SR) and principal component analysis dimensionality reduction methods are employed to identify the most salient features, which are then passed to the linear discriminant analysis (LDA) classifier. EMG and axial acceleration signal datasets from six intact-limbed and four amputee participants exhibited an average classification error of 15.68 % based on SR dimensionality reduction using the LDA classifier

Deep Time-Series Clustering: A Review

We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives