CHIMERA seminar with Professor Derek Hill

13 June 2023, 11:00 am–12:00 pm

Chimera Seminar Series

More meaningful measures of patient function by combining activity and position

This event is free.

Event Information

Open to







Alice Hardy


Executive Suite 103
Engineering Front Building
1-4 Malet Place

Please note, this is a hybrid event. You are welcome to join in-person (UCL staff and students only) or online.

It is often proposed that tracking technology  can help better understand the symptoms of people living with chronic illness, and how well they can live independently, and pick up early warning signs of risks that might need proactive management.  There are two common ways of tracking activities of patients over time in their homes:

  • to detect where they are for example, with fixed motion detectors (eg: PIR or ToF).
  • To assess their movement using wearable motion sensors (eg: accelerometer, sometimes with addition of gyroscope, magnetometer or pressure sensor)

Fixed motion sensors can measure, for example, visits to the bathroom or kitchen, and how many times people move between rooms, but are limited in their ability to distinguish between people in multi-person households, or whether someone is being active or sedentary in a given room.  Motion sensors can measure activity over time, but can’t localise this without the house.

We have implemented a method that combines both: a wearable bracelet sensor that contains its own sensors and can also receive information from room-mounted sensors, using technology developed by Panoramic Digital Health. The data collected includes the movement sensor data from accelerometer, magnetometer and barometric pressure sensor in the bracelet, labelled with the signal strength of in-room Bluetooth beacons (and values of environment sensors they contain).  The combination of these data provides a signature that combines both position and physical activity information, which we believe is potentially better able to capture meaningful measures of function than either location or activity alone.  We are running three studies collecting this data in a research setting:

  •  We also have a validation study in which individual used the technology in a “living lab” with video surveillance providing a “gold standard” reference
  • patients with chronic fatigue symptoms (and healthy controls) who also provide a numeric fatigue score several times per day
  • pairs of co-habiting individuals, where we try to look for differences in activities and behaviours depending on whether the individuals are together or apart.

In this presentation, I will show examples of the data, and the potential value of these combined measures.

We are looking for data scientists who are interested in considering how to analyze this data in more sophisticated ways to the basic ones we are currently using. We can provide anonysed access to this data for anyone who would like to do some timeseries analysis of the combined activity and location data and look for signatures that  may help us interpret this rich source of data.

About the Speaker

Professor Derek Hill

Derek Hill is Professor of Digital Health at UCL in London, England, and also CEO of Panoramic Digital Health in Grenoble France.

Derek has spent more than 25 years working in both academia and industry on innovative digital technologies that provide insights into disease progression and treatment response. His particular focus has been on technologies that can be used in clinical trials in neurodegenerative diseases to enrich clinical trial populations, and measure treatment safety and efficacy.

Derek’s initial work was based on imaging biomarkers, but in the last 10 years has become focused on wearable digital health technologies. He has been involved in developing these technologies (both hardware and software) and deploying them in clinical studies from single centre studies – to global phase 3 trials, and also alongside marketed drugs. He has more than 200 journal publications in these areas.

Current research interest is controlling variability in home-based measurements using wearable sensors, and combining data from wearable and environmental sensors to provide signatures of activities and behaviours in patients with chronic illness.