Prof Luz's research seeks to harness the power of ubiquitous digital technology for the creation of novel biomarkers for objective, scalable and cost-effective measurement of physiology and behaviour within the broad field of precision medicine. This research has the potential to deliver meaningful impact on health care in Scotland and the UK, and to revolutionise care in low- and middle-income countries.  While he has investigated methods and applications of digital phenotyping in several areas, his main focus has been on digital biomarkers of neurodegenerative diseases. He has conducted analyses of dementia data, including novel digital (behavioural) biomarkers that can be collected frequently, unobtrusively, and at scale through mobile and ambient technology. His lab have developed novel methods for the analysis of bioacoustical markers for detection and assessment of progression of Alzheimer’s dementia and other conditions.   These models have achieved state-of-the-art categorisation results for Alzheimer's detection, reaching approximately 93% accuracy in monologue data. The Luz Lab's language-independent dialogue models reach 89% accuracy using acoustic features only.

Prof Luz has also led the development of methodology, shared data sets and resources for the assessment of voice, speech and language biomarkers. As shown by a systematic review he conducted recently, research in this area has grown considerably in the last few years. However, the field remains fragmented, and adequate assessment of the different approaches and ultimately translation to clinical practice, is hindered by a lack of shared data sets and poor standardisation of modelling and evaluation methods. To address this issue, he created, in cooperation with Prof Brian MacWhinney and colleagues at Carnegie Mellon University and Edinburgh, the first international shared machine learning task on dementia detection and assessment, the ADReSS (Alzheimer's Dementia Recognition from Spontaneous Speech) Challenge. ADReSS provided acoustically normalised, pre-processed, longitudinal spontaneous speech data sets, matched for gender and age, and a platform for evaluation of machine learning models for discriminative (Alzheimer’s detection and prediction of progression from mild cognitive impairment to dementia) and regression tasks (prediction of neuropsychological test scores). This and subsequent shared signal processing and machine learning tasks have attracted many participating teams from the world's top academic and industrial laboratories.

Research on digital biomarkers is particularly promising in relation to global health. Prof Luz's research in this field has opened new avenues for the deployment of low-cost devices for health monitoring in low- and middle-income countries (as well as in high income countries). He led, for instance, the advanced technologies work package of the EU-funded SAAM project, which investigated the use of data extracted from smart electrical meters and ambient sensors for monitoring the physical and mental wellbeing of older people living independently or in assisted living care, in low-income communities in Bulgaria. This work was done in cooperation with local community workers of the Bulgarian Red Cross and Caritas. The Edinburgh team developed ambient hardware for voice, temperature, gait and gesture data collection, which we incorporated into a mental wellbeing model. Adapted versions of this model have been used since in several predictive models for depression and mood assessment based on acoustic features extracted from speech. In relation to dementia, more specifically,  these digital technologies have great potential to foster the development of low-cost, scalable and accessible tools for monitoring of cognitive function, dementia screening, and support for community-based prevention and care in low- and middle-income countries.