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Use of autism diagnosing standards in low-income countries (LICs) are restricted due to the high price and unavailability of trained health professionals. Furthermore, these standards are heavily skewed towards developed countries and LICs are underrepresented. Due to such constraints, many LICs use their own ways of assessing autism. This is the first retrospective study to analyze such local practices in Sri Lanka. The study was conducted at Ward 19B of Lady Ridgeway Hospital (LRH) using the clinical forms filled for diagnosing ASD. In this study, 356 records were analyzed, from which 79.5% were boys and the median age was 33 months. For each child, the clinical form together with the Childhood Autism Rating Scale (CARS) value were recorded. In this study, a Clinically Derived Autism Score (CDAS) is obtained from the clinical forms. Scatter plot and Pearson product moment correlation coefficient were used to benchmark CDAS with CARS, and it was found CDAS to be positively and moderately correlated with CARS. In identifying the significant variables, a logistic regression model was built based on clinically observed data and it evidenced that "Eye Contact," "Interaction with Others," "Pointing," "Flapping of Hands," "Request for Needs," "Rotate Wheels," and "Line up Things" variables as the most significant variables in diagnosing autism. Based on these significant predictors, the classification tree was built. The pruned tree depicts a set of rules, which could be used in similar clinical environments to screen for autism. LAY SUMMARY: Screening and diagnosing autism in low-income countries such as Sri Lanka has always been a challenge due to limited resources and not being able to afford global standards. Due to these challenges, locally developed clinical forms have been used. This study is the first to analyze a clinical record set for autism in Sri Lanka to benchmark the local clinic form with a global standard. Furthermore, this study identifies the most significant diagnostic symptoms for children and based on these significant features, a simple set of IF-THEN rules are derived which could be used for screening autism in a similar clinical environment by health officials in the absence of consultants.

Artificial vision using computational models that can mimic biological vision is an area of ongoing research. One of the main themes within this research is the study of the retina and in particular, retinal ganglion cells which are responsible for encoding the visual stimuli. A common approach to modelling the internal processes of retinal ganglion cells is the use of a linear - non-linear cascade model, which models the cell's response using a linear filter followed by a static non-linearity. However, the resulting model is generally restrictive as it is often a poor estimator of the neuron's response. In this paper we present an alternative to the linear - non-linear model by modelling retinal ganglion cells using a number of machine learning techniques which have a proven track record for learning complex non-linearities in many different domains. A comparison of the model predicted spike rate shows that the machine learning models perform better than the standard linear - non-linear approach in the case of temporal white noise stimuli.

The retina acts as the primary stage for the encoding of visual stimuli in the central nervous system. It is comprised of numerous functionally distinct cells tuned to particular types of visual stimuli. This work presents an analytical approach to identifying contrast-driven retinal cells. Machine learning approaches as well as traditional regression models are used to represent the input-output behaviour of retinal ganglion cells. The findings of this work demonstrate that it is possible to separate the cells based on how they respond to changes in mean contrast upon presentation of single images. The separation allows us to identify retinal ganglion cells that are likely to have good model performance in a computationally inexpensive way.

Activity recognition relates to the automatic visual detection and interpretation of human behaviour and is emerging as an active domain of computer vision. It has important applications such as identifying individuals who are at risk of suicide in public locations such as bridges or railway stations. These individuals are known to exhibit easily observable activities and behaviours such as pacing, looking up and down the railway tracks, and leaving objects on the platform. In order to detect these behaviours, an approach to individual person activity recognition is needed which can run in real time and monitor multiple individuals in parallel. We present a method for human activity recognition using skeletal keypoints and investigate how using varying sample rates and sequence lengths impacts accuracy. The results show that for any given sequence length, optimising the sample rate can result in an overall increase in classification accuracy and improvement in run-time. Results demonstrate that finding the optimal time period over which to sample frames is more important than simply decreasing the number of frames sampled. Further, we show that keypoint based activity recognition approaches outperform other state of the art approaches. Finally, we show that this approach is fast enough for real time activity recognition when up to 14 people are present in the image whilst maintaining a high degree of accuracy.

Measuring social signals has often proved challenging as they are often characterized by subtle movements which are difficult to detect. Head pose is one such social signal used to indicate where an individual’s attention is focused. This paper will discuss the problem of head pose estimation by defining the problem in terms of two fields of view, pan and tilt. A novel approach for head pose estimation is described that uses histogram of oriented gradients with support vector machines. The approach is compared with a template matching approach, among others, using a well-known dataset. The results show that the histogram of oriented gradients approach is able to determine head pan to within one class approximately 72% of the time, and head tilt to within one class approximately 71% of the time.

With the ever-increasing global population and average life expectancy, care homes and care at home services are continuously being stretched beyond capacity. Recent developments in tactile sensing have enabled robot systems to measure human vital signs such as beats per minute (BPM), Respiratory Rate (RR) and Capillary Refill Time (CRT). Using robotic systems to measure vital sign data in the home of an elderly or disabled person would greatly assist medical and health services. This paper proposes the use of a vital sign measuring robotic system together with Cloud computing to intelligently process big data and ascertain the current health status of the service user without the need to expose their identity or burden health professionals. Furthermore, a method that enables medical professionals to visualise the data for a complete geographical region as well as for individual patients is presented and hence we provide details of a closed loop system to support ageing-in-place.

Determining the receptive field of a retinal ganglion cell is critically important when formulating a computational model that maps the relationship between the stimulus and response. This process is traditionally undertaken using reverse correlation to estimate the receptive field. By stimulating the retina with artificial stimuli, such as alternating checkerboards, bars or gratings and recording the neural response it is possible to estimate the cell’s receptive field by analysing the stimuli that produced the response. Artificial stimuli such as white noise is known to not stimulate the full range of the cell’s responses. By using natural image stimuli, it is possible to estimate the receptive field and obtain a resulting model that more accurately mimics the cells’ responses to natural stimuli. This paper extends on previous work to seek further improvements in estimating a ganglion cell’s receptive field by considering that the receptive field can be divided into subunits. It is thought that these subunits may relate to receptive fields which are associated with bipolar retinal cells. The findings of this preliminary study show that by using subunits to define the receptive field we achieve a significant improvement over existing approaches when deriving computational models of the cell’s response.

Determining the location of individuals within indoor locations can be useful in various scenarios including security, gaming and ambient assisted living for the elderly. Healthcare services globally are seeking to allow people to stay in their familiar home environments longer due to the multitude of benefits associated with living in non-clinical environments and technologies to determine an individual's movements are key to ensuring that home emergencies are detected through lack of movement can be responded to promptly. This paper proposes a device-free localisation (DFL) system which would enable the individual to proceed with normal daily activities without the concern of having to wear a traceable device. The principle behind this is that the human body absorbs/reflects the radio signal being transmitted from a transmitter to one or more receiving stations. The proposed system design procedure facilitates the use of a minimum number of wireless nodes with the help of a principle component analysis (PCA) based intelligent signal processing technique. Results demonstrate that human detection and tracking are possible to within 1m resolution with a minimal hardware infrastructure.

Determining the location of individuals within indoor locations can be useful in various scenarios including security, gaming and ambient assisted living for the elderly. Healthcare services globally are seeking to allow people to stay in their familiar home environments longer due to the multitude of benefits associated with living in non-clinical environments and technologies to determine an individual's movements are key to ensuring that home emergencies are detected through lack of movement can be responded to promptly. This paper proposes a device-free localisation (DFL) system which would enable the individual to proceed with normal daily activities without the concern of having to wear a traceable device. The principle behind this is that the human body absorbs/reflects the radio signal being transmitted from a transmitter to one or more receiving stations. The proposed system design procedure facilitates the use of a minimum number of wireless nodes with the help of a principle component analysis (PCA) based intelligent signal processing technique. Results demonstrate that human detection and tracking are possible to within 1m resolution with a minimal hardware infrastructure.

The processing capabilities of the biological vision system are still vastly superior to artificial vision, which has been an active area of research for over half a century. Current artificial vision techniques, motivated by this robust performance, integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power and performance. With respect to modelling the retina, this is due to an insufficient understanding of the complex interactions between the cells and their organisation within the system. The core components within this system are the retinal ganglion cells as they convey the accumulated data of real world images as action potentials onto the visual cortex via the optic nerve. Computational models that approximate the processing that occurs within these visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where the input is a known and its output is recorded. Techniques capable of mapping this input-output response involve computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this work we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model ganglion cell \ud behaviour, and are a viable alternative to traditional linear-nonlinear approaches.

This paper describes the application of a Convolutional Neural Network (CNN) in the context of a predator/prey scenario. The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey). The CNN is driven by both conventional image frames and dynamic vision sensor "frames" that consist of a constant number of DAVIS ON and OFF events. The network is thus "data driven" at a sample rate proportional to the scene activity, so the effective sample rate varies from 15 Hz to 240 Hz depending on the robot speeds. The network generates four outputs: steer right, left, center and non-visible. After off-line training on labeled data, the network is imported on the on-board Summit XL robot which runs jAER and receives steering directions in real time. Successful results on closed-loop trials, with accuracies up to 87% or 92% (depending on evaluation criteria) are reported. Although the proposed approach discards the precise DAVIS event timing, it offers the significant advantage of compatibility with conventional deep learning technology without giving up the advantage of data-driven computing., Paper presented at the conference: Second International Conference on Event-Based Control, Communication and Signal Processing (EBCCSP) 2016, At Krakow, Poland

Robotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user's activities and changing user's habits. European Commission. EC | FP7 | SP1 | ICT . FP7-ICT-2009-6.

Modelling aspects of the human vision system, including the retina, is difficult due to insufficient knowledge about the internal components, organisation and complexity of the interactions within the system. Retinal ganglion cells are considered a core component of the human visual system as they convey the accumulated data as action potentials onto the optic nerve. Current techniques capable of mapping this input-output response involve computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. This paper aims to model a retinal ganglion cell with a simple spiking neuron combined with a pre-processing method, which accounts for the preceding retinal neural structure. Performance of the models is compared with the spike responses obtained in the electrophysiological recordings from a mammalian retina subjected to visual stimulation.

Even though artificial vision has been in development for over half a century it still fares poorly when compared to biological vision. The processing capabilities of biological visual systems are vastly superior in terms of power, speed, and performance. Inspired by this robust performance artificial vision systems have sought to take inspiration from biology by modeling aspects of biological vision systems. Existing computational models of visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where a known input is given to the system and its output is recorded. These models need to capture the full spatio-temporal description of neuron behaviour under natural viewing conditions. In this work we use state-of-the-art fuzzy neural network techniques to accurately model the responses of retinal ganglion cells. We illustrate how a self-organising fuzzy neural network can accurately model ganglion cell behaviour, and are a viable alternative to traditional system identification techniques.

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work European Comission, FP7 Programme

Determining the location of individuals within indoor locations can be useful in various scenarios including security, gaming and ambient assisted living for the elderly. Healthcare services globally are seeking to allow people to stay in their familiar home environments longer due to the multitude of benefits associated with living in non-clinical environments. Technology to determine an individual's movements or lack of movement is key to ensuring that home emergencies are detected and responded to promptly. This paper proposes a device-free wireless-based localisation (DFL) system which would enable the individual to proceed with normal daily activities without the constraint of having to wear a traceable device. The principal behind this is that the human body absorbs/reflects the radio signal being transmitted from a transmitter to one or more receiving stations. The system design procedure facilitates the use of a minimum number of wireless nodes with the aid of a principle component analysis (PCA) based intelligent signal processing technique. Results demonstrate that human detection and tracking are possible to within 1m resolutions with a minimal wireless hardware infrastructure. (6 pages)

Determining the location of individuals within indoor locations can be useful in various scenarios including security, gaming and home assistance. The domain of home assistance also includes caring for the elderly. Healthcare services globally are seeking to allow people to stay in their familiar home environments longer due to the multitude of benefits associated with living in non-clinical environments and technologies to determine an individual's movements are key to ensuring home emergencies detected through lack of movement can be responded to promptly. This paper proposes a device-free technique which would enable the individual to proceed with normal daily activities without the concern of having to wear a traceable device. The principle behind this is that the human body will absorb/reflect the signal being transmitted from a transmitter to one or more monitoring stations. Results are presented which demonstrate that human detection and tracking is possible to within 1.5m resolution.