From sensors to software: an integrated ecosystem that combines neuroscience with generative artificial intelligence to transform stress management and business coaching.
Over the past twenty years, the topic of stress and cognitive load in high-pressure work environments has become central not only in clinical settings, but also in industrial safety, productivity, and organizational wellbeing. Yet, while PPE have evolved to protect from mechanical, chemical, or environmental risks, the psychophysiological dimension of workers has remained almost entirely unexplored territory.
This is the space in which Ianunai operates—an innovative startup bringing a completely new approach to the field: a wearable technology capable of reading and interpreting, in real time, a person’s stress level, attention, and mental workload.
The vision originates from Henesis, an Italian organization that since 2007 has developed enabling technologies through Open Innovation programs. Today, Henesis is part of the Ianunai group: a transition that combines scientific continuity with entrepreneurial momentum, transforming years of research and experimentation into solutions designed for the most complex operational contexts.
Leading this journey is Luca Ascari, CEO of Ianunai, who brings over twenty years of experience in human–machine interaction and applied neuroscience, developed in collaboration with Toyota Motor Europe and some of Italy’s top research institutes.
What scientific and technological path led you to Ianunai?
Ianunai was born directly from the experience gained at Henesis, where for years we collaborated with Toyota Motor Europe on advanced interfaces with the nervous system. We’re talking about electroencephalographic, cardiac, and electrodermal biosignals: weak signals, difficult to interpret, but extremely rich in information about a person’s internal state. Over twenty years we generated know-how, AI algorithms, and several patents on models capable of recognizing drowsiness, anticipating driving behaviors, and interpreting emotions. The most important work was learning how to make all of this function outside the laboratory, with just a few sensors and wearable platforms. That’s where Ianunai’s technological ecosystem comes from.
How did you arrive at the idea of using a wearable sensor to monitor stress?
Stress is a complex condition involving cognitive and emotional dimensions as well as physiological responses that vary from person to person. The challenge is to build AI models that are reliable both for people the system has never seen before and for individuals over time, adapting to each user. We worked extensively to identify measurable physiological correlates and link them to specific internal states—from EEG signals to cardiac and electrodermal activity. At the same time, we studied the physiological correlates of stress-mitigation techniques, so we could show people—in real time—the tangible effect of practices such as mindfulness and controlled breathing.
What challenges did you encounter in developing the devices?
The main devices are two: one handheld and one applied to the forehead. The forehead device is the most delicate because it must work while the person is actively working. Challenges involve long-term comfort—breathability, adhesion, minimal thickness—and managing motion artifacts and electromagnetic noise, which are significant because electrophysiological signals are extremely weak. Moreover, only a few sensors can be used in the field, so you must extract a lot from very little. And then there’s the issue of context: people move, interact, and change environments, and this affects how signals are interpreted.
How did you solve these problems?
Our solutions cover all stages, from acquisition to delivering final information. Many are the result of research and are now patented. One example is FibroTec, a material we developed with CNR-IMEM in Parma: starting from silk fibroin, we created a breathable, adhesive, conductive, and compostable compound. It’s ideal for producing electrodes that eliminate friction on the skin and therefore motion artifacts—one of the biggest issues in acquiring clean signals.
Other solutions are software-based: algorithms that estimate noise, recognize artifacts, and complex models that run on smartphones—or on servers during training. But ultimately everything must run on the device itself, because you can’t rely on having connectivity in the field.
Which physiological parameters do you analyze? What role does AI play?
We acquire signals from the autonomic nervous system—cardiac and electrodermal activity—along with inertial signals, neocortical EEG, and acoustic signals. Artificial intelligence cleans the signals, recognizes context, fuses information, extracts patterns, and adapts models to each individual over time. Some models, such as those designed to assess attention in critical moments, are more stable; others evolve progressively with use.
How do the handheld and forehead devices complement each other?
They are largely complementary. The handheld device is ideal for daily “de-stress” sessions. Using validated mindfulness, breathing, and meditation techniques, it helps bring the body back to baseline and prevent stress accumulation. Seeing physiological parameters change in real time makes the experience much more tangible.
The forehead device, on the other hand, accompanies the person while working. It monitors cognitive load and attention. When attention drops, it automatically performs a brief assessment—using acoustic or vibrational stimuli integrated into PPE—to verify whether the person is still cognitively present. It’s quick, non-invasive, and does not increase cognitive load.
How do you ensure the technology remains minimally invasive?
We developed all components modularly so they can be easily integrated into PPE: helmets, under-helmets, surgical caps. In some cases a nearly invisible forehead sensor can be used—sufficient to monitor attention, for example while driving. FibroTec allows us to build electrodes in different shapes and thicknesses while maintaining naturalness and comfort.
How are the data used, and what do companies see?
Data are processed in full compliance with GDPR, with servers located in Europe and Henesis’s medical-device expertise ensuring security across the entire chain. Companies do not see personal data—only aggregated correlates linked to KPIs such as absenteeism, leave, incidents, and sentiment. The shift supervisor receives notifications if there is a potentially risky situation in the team. The individual is the first to be alerted if a break is needed. Privacy is never compromised: workers remain the owners of their data. For this reason, before launch, we shared the project with unions and occupational physicians, obtaining their approval.
What role does the software platform play?
The platform has two layers. The first is personal: it allows individuals to use the devices at home as well, because stress is not confined within the workplace. The second is organizational: each role sees only what is relevant. Users receive their own alerts, shift supervisors see their team, HR sees aggregated data on breaks and sentiment, and occupational physicians can investigate where necessary. And everything must function even without a connection—this is essential in operational environments.
Will stress monitoring become standard in high-risk contexts?
I believe so. Awareness of one’s mental well-being is still limited, especially in operational teams. Stress-management techniques are widespread among white-collar workers, less so among those working in the field. Our technology can democratize access to these practices, bring them to blue-collar workers, and improve safety and prevention.
We have been working for years with scientific partners such as the CNR Institute of Neuroscience and CNR-IMEM in Parma, and the Universities of Padua and Trieste, including within European clinical projects. It’s a path where research and real-world impact move forward together.
To those who fear that such advanced technology may be “too much,” what do you say?
Technology will never replace human beings, but it can increase our awareness of what happens inside us. The more advanced it is, the less visible it should be. For it to work, trust is essential. Privacy must be guaranteed, and cybersecurity must protect the entire data chain, from acquisition to use. In the sports world we’ve already seen examples of data leaks—company seriousness is crucial. In Europe we have strong regulations like GDPR and the AI Act, which protect individuals. Within this framework, technology can truly support well-being and safety.
