  Activity and motion analysis has the potential to be used as a diagnostic
tool for mental disorders. However, to-date, little work has been performed in
turning stratification measures of activity into useful symptom markers. The
research presented in this thesis has focused on the identification of
objective activity and behaviour metrics that could be useful for the analysis
of mental health symptoms in the above mentioned dimensions. Particular
attention is given to the analysis of objective differences between disorders,
as well as identification of clinical episodes of mania and depression in
bipolar patients, and deterioration in borderline personality disorder
patients. A principled framework is proposed for mHealth monitoring of
psychiatric patients, based on measurable changes in behaviour, represented in
physical activity time series, collected via mobile and wearable devices. The
framework defines methods for direct computational analysis of symptoms in
disorganisation and psychomotor dimensions, as well as measures for indirect
assessment of mood, using patterns of physical activity, sleep and circadian
rhythms. The approach of computational behaviour analysis, proposed in this
thesis, has the potential for early identification of clinical deterioration in
ambulatory patients, and allows for the specification of distinct and
measurable behavioural phenotypes, thus enabling better understanding and
treatment of mental disorders.
