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Area 2: Human Communication and Behavior


Communicating humansIn this area we develop models of how humans perceive and produce non-verbal communication. This can be used both to gain understanding about the mechanisms underlying human communication and behavior, and also to design systems where communication and behavior understanding is used, e.g. for computerized analysis of cognitive decline.


Research Engineers

MSc Students

  • Magnus Ruben Tibbe
  • Fanxuan Liu (MSc 2024)
  • Ioannis Athanasiadis (MSc 2022)
  • Frans Nordén (MSc 2021)
  • Olga Mikheeva (MSc 2017)

PhD Students

  • Chen Ling
  • Olga Mikheeva
  • Taras Kucherenko (PhD 2021, now at SEED, Sweden)
  • Judith Bütepage (co-supervisor, PhD 2019, now at SEED, Sweden)
  • Kalin Stefanov (co-supervisor, PhD 2018, now at Monash University, Australia)

Post Docs


Current Projects

Detecting behavioral bio-markers (SeRC 2023-present)

In this project, which is a collaboration with the Department of Women’s and Children’s health at Karolinska Institutet, we develop representation learning methods to detect various kinds of bio-markers, typically connected to underlying motor or cognitive conditions, from non-verbal behavior. The currently primary application is detection of motor conditions in neonates, but we will also work with datasets from other applications, containing 3D body, face, hand pose, gaze behavior, RGB video, IMU data, and other kinds of measurements. The underlying mechanisms are modeled using deep generative approaches such as VAE and Diffusion Models.


UNCOCO: UNCOnscious COmmunication (WASP 2023-present)


This project, which is part of the WARA Media and Language and a collaboration with the Perceptual Neuroscience group at KI, entails two contributions.

Firstly, we develop a 3D embodied, integrated representation of head pose, gaze and facial micro expression, that can be extracted from a regular 60 Hz video camera and a desk-mounted gaze sensor. The embodied, integrated 3D representation of head pose, gaze and facial micro expression provides a preprocessing step to the second contribution, a deep generative model for inferring the latent emotional state of the human from the non-verbal communicative behavior. The model is employed in three different contexts: 1) estimating user affect for a digital avatar, 2) analyzing human non-verbal behavior connected to sensor stimuli, e.g., quantify approach/avoidance motor response to smell, 3) estimating frustration in a driving scenario.


STING: Synthesis and analysis with Transducers and Invertible Neural Generators (WASP 2022-present)

STING logo

Human communication is multimodal in nature, and occurs through combinations of speech,
language, gesture, facial expression, and similar signals. To enable natural interactions with human beings, artificial agents must be capable of both analysing and producing these rich and
interdependent signals, and connect them to their semantic implications. Unfortunately, even the strongest machine learning methods currently fall short of this goal: automated semantic understanding of human behaviour remains superficial, and generated agent behaviours are empty gestures lacking the ability to convey meaning and communicative intent.

The STING NEST, part of the WARA Media and Language, intends to change this state of affairs by uniting synthesis and analysis with transducers and invertible neural models. This involves connecting concrete, continuous­ valued sensory data such as images, sound, and motion, with high­ level, predominantly discrete, representations of meaning, which has the potential to endow synthesis output with human­ understandable high­level explanations, while simultaneously improving the ability to attach probabilities to semantic representations. The bi­directionality also allows us to create efficient mechanisms for explainability, and to inspect and enforce fairness in the models.
Recent advances in generative models suggest that our ambitious research agenda is likely to be met with success. Normalising flows and variational autoencoders permit both extract­ing disentangled representations of observations, and (re­-)generating observations from these abstract representations, all within a single model. Their recent extensions to graph­ structured data are of particular interest because graphs are commonly­ used semantic representations.
This opens the door not only to generating structured information, but also to capturing the com­position of the generation itself (which is a graph in its own right) by exploiting and transferring techniques from finite ­state transducers and graph grammars.


Project home page

Past Projects

EACare: Embodied Agent to support elderly mental wellbeing (SSF, 2016-2021)

The main goal of the multidisciplinary project EACare is to develop an embodied agent – a robot head with communicative skills – capable of interacting with especially elderly people at a clinic or in their home, analyzing their mental and psychological status via powerful audiovisual sensing and assessing their mental abilities to identify subjects in high risk or possibly at the first stages of cognitive decline, with a special focus on Alzheimer’s disease. The interaction is performed according to the procedures developed for memory evaluation sessions, the key part of the diagnostic process for detecting cognitive decline.
This new diagnostic system will be one of the means by which medical doctors evaluate people for cognitive decline, in parallel to the existing methods such as memory evaluation sessions with a (human) clinician, MRI scans, blood tests, etc. Different parts of the framework can also be used for other purposes, such as to develop tools for dementia preventive training and for decision support during clinical memory evaluation sessions.


Project home page

Data-driven modelling of interaction skills for social robots (KTH ICT-TNG 2016-2018)

This project aims to investigate fundamentals of situated and collaborative multi-party interaction and collect the data and knowledge required to build social robots that are able to handle collaborative attention and co-present interaction. In the project we will employ state-of-the art motion- and gaze tracking on a large scale as the basis for modelling and implementing critical non-verbal behaviours such as joint attention, mutual gaze and backchannels in situated human-robot collaborative interaction, in a fluent, adaptive and context sensitive way.


HumanAct: Visual and multi-modal learning of Human Activity and interaction with the surrounding scene (VR, EIT ICT Labs 2010-2013)

The overwhelming majority of human activities are interactive in the sense that they relate to the world around the human (in Computer Vision called the "scene"). Despite this, visual analyses of human activity very rarely take scene context into account. The objective in this project is modeling of human activity with object and scene context.

The methods developed within the project will be applied to the task of Learning from Demonstration, where a (household) robot learns how to perform a task (e.g. preparing a dish) by watching a human perform the same task.