Towards a Sensor-based System for Assessing and Monitoring Powered Mobility Skills in Children

Children with motor or cognitive impairments who require powered mobility at a very young age will face social and environmental barriers that make learning how to use the mobility device a challenging task. We present a first approach of a framework to help therapists and service providers to assess and monitor how children use their mobility device, which results from the combination of a plug and play inertial sensor, and the support of the Assessment Learning tool (ALP) from Nilsson and Durkin. We performed a formative study on four able-bodied children using an electric wheelchair. Results suggest it is possible to measure children's driving skills with this approach, and that results can be mapped to the validated ALP tool. We present the limitations of our study and the direction of future work

Playground social interaction analysis using bespoke wearable sensors for tracking and motion capture

Unstructured play is considered important for the social, physical and cognitive development of children. Traditional observational research examining play behaviour at playtime (recess) has been hampered by challenges in obtaining reliable data and in processing sufficient quantities of that data to permit credible inferences to be drawn. The emergence of wearable wireless sensor technology makes it possible to study individual differences in childhood social behaviour based on collective movement patterns during playtime. In this work, we introduce a new method to enable simultaneous collection of GNSS/IMU data from a group of children interacting on a playground. We present a detailed description of system development and implementation before going on to explore methods of characterising social groups based on collective movement recording and analysis. A case study was carried out for a class of 7-8 year old children in their school playground during 10 episodes of unstructured play. A further 10 play episodes were monitored in the same space following the introduction of large, loose play materials. This study design allowed us to observe the effect of an environmental intervention on social movement patterns. Sociometric analysis was conducted for comparison and validation. This successful case study demonstrates that sensor based movement data can be used to explore children’s social behaviour during naturalistic play.LEGO Foundatio

Towards a Wearable Wheelchair Monitor: Classification of push style based on inertial sensors at multiple upper limb locations

Measuring manual wheelchair activity by using wearable sensors is becoming increasingly common for rehabilitation and monitoring purposes. Until recently most research has focused on the identification of activities of daily living or on counting the number of strokes. However, how a person pushes their wheelchair - their stroke pattern - is an important descriptor of the wheelchair user's quality of movement. This paper evaluates the capability of inertial sensors located at different upper limb locations plus the wheel of the wheelchair, to classify two types of stroke pattern for manual wheelchairs: semicircle and arc. Data was collected using bespoke inertial sensors with a wheelchair fixed to a treadmill. Classification was completed with a linear SVM algorithm, and classification performance was computed for each sensor location in the upper limb, and then in combination with wheel sensor. For single sensors, forearm location had the highest accuracy (96%) followed by hand (93%) and arm (90%). For combined sensor location with wheel, best accuracy came in combination with forearm. These results set the direction towards a wearable wheelchair monitor that can measure the quality as well as the quantity of movement and which offers multiple on-body locations for increased usability

The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery

The International Human Epigenome Consortium (IHEC) coordinates the generation of a catalog of high-resolution reference epigenomes of major primary human cell types. The studies now presented (see the Cell Press IHEC web portal at http://www.cell.com/consortium/IHEC) highlight the coordinated achievements of IHEC teams to gather and interpret comprehensive epigenomic datasets to gain insights in the epigenetic control of cell states relevant for human health and disease

“Want to come play with me?” Outlier subgroup discovery on spatio-temporal interactions

Our lives are made of social interactions which can be recorded through personal gadgets as well as sensors capturing ubiquitous and social data. This type of data, such as spatiotemporal data from the real-time location of people, for example, can then be used for inferring interactions which can be translated into behavioural patterns. In this paper, we consider the automatic discovery of exceptional social behaviour from spatio-temporal interaction data, focusing on two areas: exceptional subgroups and spatiotemporal outliers – both in the form of descriptive patterns. For that, we propose a method for exceptional social behaviour discovery, combining subgroup discovery and network science methods for identifying behaviour that deviates from the norm. We also propose the use of two outlier detection metrics for identifying outliers, namely the Local Outlier Factor (LOF) and the Voronoi area. We applied the proposed method on synthetic data as well as two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data