The introduction of smart meters has allowed us to monitor consumers' energy usage with a high degree of granularity. Detailed electricity usage patterns and trends can be identified to help understand daily consumer habits and routines. The challenge is to exploit these usage patterns and recognise when sudden changes in behaviour occur. This would allow detailed, around the clock, monitoring of a person's wellbeing and would be particularly useful for tracking individuals suffering from self-limiting conditions such as Alzheimer's, Parkinson's disease and clinical depression. This paper explores this idea further and presents a new approach for unobtrusively monitoring people in their homes to support independent living. The posited system uses data classification techniques to detect anomalies in behaviour through personal energy usage patterns in the home. Our results show that it was possible to obtain an overall accuracy of 99.17% with 0.989 for sensitivity, 0.995 for specificity and an overall error of 0.008 when using the VPC Neural Network classifier.