WATCH / RESEARCH NOTES · ROSALIND PICARD / AFFECTIVE COMPUTING
I watched three pieces of work connected to Rosalind Picard and the MIT Media Lab’s Affective Computing group: Emotion Technology: From Autism to Customer Experience and Decision Making, An AI smartwatch that detects seizures, and Research Group Overview: Affective Computing. Watching them together was much more useful than treating them as separate references because they show the progression from physiological sensing, to real-world wearable systems, to the wider field of affective computing.
I also found Picard’s work genuinely fascinating. What I like about it is that she moves very comfortably between engineering, physiology, AI, healthcare and human experience. The technology is clever, but the interesting part is always what that technology can actually do for a person.
Picard uses the term affective computing for technology that attempts to sense, interpret or respond to information connected to emotion, arousal and human state. This immediately felt relevant to my project because some of her earlier work looks specifically at autism and at situations where a person’s internal physiological state may not be obvious from outward behaviour.
That matters for my project because airport support often depends on the traveller recognising that they need help and then communicating that need. A wearable system could potentially notice a meaningful physiological change earlier and offer support privately, without assuming that the person must visibly appear distressed first.
However, this does not mean the technology can simply “read emotion”. The stronger interpretation for my project is that it can notice changes in physiological activity that may be meaningful when combined with context and the user’s own interpretation.
One of the parts I found confusing in the first talk was Picard’s discussion of the autonomic nervous system.
The autonomic nervous system controls many bodily processes that happen largely without conscious effort, including heart rate, sweating, digestion and parts of breathing.
The sympathetic nervous system is associated more with activation and preparing the body for action, while the parasympathetic nervous system is associated more with slowing, recovery and regulation. They are not simply a stressed/calm switch, but this distinction helps me understand why signals such as EDA, heart rate and HRV change.
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Useful way for me to think about it: the wearable would not be reading an emotion directly. It would be reading changes in autonomic activity that may indicate increased physiological demand or arousal.
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Across Picard’s work, skin conductance / electrodermal activity (EDA) appears repeatedly. EDA changes with sweat-gland activity and is closely linked to sympathetic arousal.
What I found especially useful is that Picard repeatedly shows how easily physiological signals can be misinterpreted. EDA can change with emotional stress, but also with cognitive load, excitement, neurological activity and other forms of activation.
This is directly relevant to my project. If an autistic traveller’s EDA or heart rate changes, the system cannot automatically know whether the cause is sensory overload, anxiety, walking quickly, carrying luggage, excitement or something else.
That reinforces the direction I have already been moving towards: personal baseline + multiple signals + context + user interpretation.
Picard’s earlier 24-hour recordings also produced surprisingly large EDA peaks during sleep. That was useful because it showed me that a strong physiological response does not necessarily map neatly onto what I would intuitively label as “stress”. The body can behave very differently across sleep, movement, attention, emotion and neurological activity.
Picard’s wider work also used real-world driving to explore physiological sensing. Drivers were monitored using signals including ECG / heart activity, skin conductance, respiration and muscle activity while researchers compared calmer and more demanding driving conditions.