Selfback

Direct costs have been estimated in one study as 1.6-3.2% of all health expenditure. Indirect costs, which are largely due to work absence, have been estimated as $11 billion in the UK.

Recent published guidelines for the management of non-specific LBP have self-management at their cornerstone, with patients being advised against bed rest, and advised to remain active, remain at work where possible, and to perform stretching and strengthening exercises, as well as avoiding long periods of inactivity. However, adherence to self-management is challenging due to lack of monitoring and feedback.

SELFBACK is an EU funded Horizon 2020 project to develop a monitoring system to assist the patients in deciding and reinforcing appropriate physical activity and exercise plans in order to self-mange LBP. The system continuously monitors users’ physical activity and sleep using a wristband. This information is combined with user-reported pain levels and functional ability to recommend a personalised self-management plan for the user. Plan achievement is automatically tracked and poor adherence results in motivational notifications (motifications) being triggered to encourage behavioural change. 

FUNDING

• Selfback Website
• RGU Selfback Website

Papers

• Learning Deep and Shallow Features for Human Activity Recognition, International Conference on Knowledge Science, Engineering and Management - KSEM 2017
• knn Sampling for Personalised Human Activity Recognition, International Conference on Case-Based Reasoning - ICCBR 2017
• Learning Deep Features for kNN-Based Human Activity Recognition, Workshop on Case-based Reasoning and Deep Learning - CBRDL 2017)
• A Siamese Convolutional Network for Developing Similarity Knowledge in the SelfBACK Dataset, Workshop on Case-based Reasoning and Deep Learning - CBRDL 2017
• Accuracy of Physical Activity Recognition from a Wrist-worn Sensor, Physiotherapy UK 2017
• SELFBACK-Activity Recognition for Self-management of Low Back Pain, SGAI International Conference on Artificial Intelligence – SGAI 2016