Aïcha Rizzotti-Kaddouri HE-Arc Engineering Neuchâtel

Portrait of Aïcha Rizzotti-Kaddouri


Professor at HE-Arc Ingénierie since 2003

Born in 1968 

Lives in Yvonand


Aïcha Rizzotti -Kaddouri

Aïcha graduated in telecommunications and holder of a master’s degree in mathematics and software engineering from Joseph Fourier University (Grenoble I). Aïcha teaches at bachelor’s and master’s levels (concurrent programming, mobile applications and wearables, HCI for mobile application, Java applications). She also runs Rapid Application Development courses for the Master of Advanced Studies (MAS-RAD) for non-computer scientist engineers.

Every week she practises various endurance sports such as spinning, kick boxing and step aerobics. She has a passion for ancient Arabian music.

At the St-Imier site, she conducts research into (wearable) IoT technology, specialising in the field of physiological and biological signal acquisition and analysis of these signals. The aim of her research: to find links between certain ailments such as type 1 diabetes and emotional factors such as stress.


What is special about your research project regarding stress?

Previous studies have shown two main signals to be the most useful for the detection of stress: heart rate variability (HRV) and transpiration, observed by means of the skin’s galvanic response, e.g. electrodermal activity (EDA). However, where such measurements were previously made under laboratory conditions, the tendency today is to conduct them under ambulatory conditions (at home, while walking, on the sports field, etc.), where a person’s activities add disturbances (artefacts) to the signal. So the pure signals are not totally accessible due to the numerous disturbances affecting them. Consequently, separating the useful data from the noise poses a major challenge in their application for detecting stress. We seek to validate the hypothesis that these parameters allow the objective measurement of a person’s stress level.

This project is being run in partnership with HES Valais, financed by HES-SO. We work mainly with a wristband produced by an MIT spin-off (https://www.empatica.com/en-int/research/e4/). The data collected are correlated with the context of the patient to enable personalisation of the result. Generally, heart rate and transpiration change with physical effort or mental stress. The level of cortisol in the body is also taken into account. This is the objective parameter, already intensively studied, on which the other parameters will be calibrated.

The data gathered by the wristband are transmitted and assembled. We then use computer programs that enable computers to learn by themselves from the available data (machine learning, which is an artificial intelligence sub-assembly in the field of computing, to extract the levels of stress for a person and the type of physical or mental stress). The data collected by the wristband are transmitted via a medical platform.

What are the next steps in the project?

We have received authorisation from the Swiss committee of ethics to study thirty candidates. They will take part in a 50-minute experiment involving wearing the wristband and using our complete system. We will play them relaxing music, they will take part in stressful games, watch film extracts and do a physical activity. In parallel with the data logged by the wristband, the patient will be able to express their feelings and emotions in notes written to accompany their data. The participant will then wear the wristband for seven days. During this time, they will also receive prompts from their smartphone to describe their psychological state.

What objectives are you aiming for in the medium to long term with this research?

If we succeed in validating this hypothesis, it will then be possible to monitor high-level athletes much more closely. For the general public, the idea would be to send people alerts so that they realise how many stress peaks they encounter in the course of the day. I hope that, in this way, it will be possible to reduce the number of cases of burnout thanks to monitoring of intense phases of stress. (Between 2000 and 2010, the number of people suffering from chronic stress in Switzerland has risen from 26.6% to 34.4%)

For a few years, you have been specialising in the medical “Quantified Self” field. What other research are you carrying out?

Based on the same principle as stress measurement, we are trying to find out whether there is a link between electrodermal activity, heart rate variation and blood sugar levels. 
Proactive detection of blood sugar levels by means of algorithms in the area of artificial intelligence could thus considerably improve the daily self-treatment of diabetes by patients and also provide endocrinology and diabetology units with a means of collecting data to analyse the continual developments of patients in real time. 
We are also working on the analysis of sleep monitoring through use of the above-mentioned signals, with a particular view to monitoring patients suffering from drowsiness.
Still in the field of IoT, I am involved in a project that aims to optimise temperature variations in a machine-tool. 

Victoria Barras