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The AmI4HC workshop will take place on October 8, in a joint session with the Computational and Affective Intelligence for Computer Assisted Interventions (AICAI) in conjugation with MICCAI 2023 (Vancouver, Canada). 

Workshop on Ambient Intelligence for HealthCare (AmI4HC)

Ambient Intelligence (AmI) defines the future vision of intelligent computing where sensors and processors are embedded into everyday devices and the environment will seamlessly adapt to the user's requirements. AmI is increasingly seeing applications in healthcare (e.g., for quality improvement, senior care at home, ensuring clinician and patient safety by monitoring staff compliance with clinical best practices or relieving staff of burdensome documentation tasks). Despite the promise of ambient intelligence to improve the quality of care, the continuous collection of large amounts of sensor data in healthcare settings presents numerous challenges from sensor technologies, data management, technical model development, and causal inference to ethical, privacy, bias, and fairness. This workshop aims to shed light on this novel technology. 


Ambient Intelligence (AmI) involves employing contactless sensors and contact-based wearable devices embedded in healthcare settings to collect data (eg, imaging data of physical spaces, audio data, or body temperature), coupled with machine learning algorithms to efficiently and effectively interpret these data. Concurrent advances in multi-modal sensing technology, data processing, machine learning, and computer vision have enabled the development of ambient intelligence—the ability to continuously and unobtrusively monitor and understand presence, actions, and interactions in physical environments. 

Boosted by innovations in data science and artificial intelligence, advanced medical imaging systems are already helping clinicians augment their capabilities to make diagnostic and treatment decisions. By contrast, monitoring the physical actions (of clinicians, patients, and family members) to inform decisions or guiding decisions about physical interventions (e.g., physical therapy) remain largely underexplored. Health-critical activities that occur in physical spaces, including hospitals, nursing facilities, and private homes could largely benefit from intelligent ambient environments. In recent years, AmI is finding its way to these applications. Advances in low-cost sensors along with machine learning breakthroughs complement existing clinical decision-support systems by providing a computer-assisted understanding of the physical activities of healthcare.

Passive, contactless sensors embedded in the environment can form an AmI that is aware of people’s movements and adapt to their continuing health needs. This technology can not only monitor a patient's health status and trajectory but also highlight the quality and nature of care delivered by the entire healthcare team. There are yet several remaining challenges to be solved to make AmI technologies usable for healthcare applications. One dimension is advancing ambient sensing technologies to produce precise, dense, accurate, and frequent measurements; another is leveraging these observations to produce prescriptive guidance to improve human health, such as determining the optimal dynamic treatment regimes.

This workshop aims to explore and advance how ambient, contactless sensors, in addition to contact-based wearable devices, can enhance the delivery of care (preventive and prescriptive care) in healthcare spaces (e.g., hospitals, nursing facilities) and daily living spaces (e.g., homes and workspaces). Submitted papers must include technical and clinical experimental analysis of data from individuals/humans in physical spaces or will include societal and ethical concerns about these new technologies in everyday spaces. 

Submissions must affirm (in the paper text) an ethics committee or institutional review board (IRB) approval, or justify a waiver, e.g. if a public dataset is used. We especially encourage submissions of papers resulting from collaboration between technical and clinical experts.


Topics of interest include (but are not limited to):

  • Intelligent Environments for Healthcare
  • Integration of Ambient Sensors with Medical Imaging, Radiology, or Neuroscience
  • Ambient Intelligence in Hospital Spaces 
    • Intensive Care Units
    • Operating Rooms
    • Other Healthcare Spaces
  • Ambient Intelligence in Daily living Spaces
    • Elderly Living Spaces and Aging
    • Chronic Disease Management
    • Mental Health
  • Behavior Recognition in Complex Scenes    
  • Learning with Big Data
  • Causal Inference and Causal Reinforcement Learning for Healthcare
  • Computational Neurorehabilitation with Ambient Sensing
  • Rare Event Detection (e.g., falls)
  • Generalization to New Environments 
  • Pervasive Computing
  • Unobstructed Embedded Devices 
  • Sensors and User Interfaces 
  • Context Awareness
  • Human Activity Recognition
  • Human Location or Pose Recovery in Physical Spaces 
  • Automated or Semi-Automated Orofacial Assessment
  • Health or Safety Monitoring
  • Assessment of Depression or Anxiety
  • Adaptive Rehabilitation Technologies
  • Longitudinal (Remote) Monitoring of Symptoms
  • Automating Critical Care Support
  • Reducing Patient Waiting Time
  • Monitoring Vital Signs 
  • Diagnosis and Care in Complicated Scenarios
  • Social and Ethical Challenges
  • Privacy in Computer Vision 
  • Bias and Fairness
  • Delivering Affordable Healthcare to Remote Rural Areas 

Link to CfP

Tentative Program Schedule (Oct 8th, 2023 / Sunday PM)

13:30-13:40Introduction and Welcome
13:40-14:10Keynote 1 - Tanveer Syeda-Mahmood, IBM (in-person)
14:10-14:30In-person oral session 
14:30-15:00Keynote 2 - Prof. Teodor Grantcharov, Stanford
15:00-15:30 Coffee Break & Playing videos of virtual presenters (5 papers)
15:30-16:00Keynote 3 - Dr. Akane Sano
Title: Multimodal Machine Learning and Human Centered Computing for Health and Wellbeing
16:00-16:30Keynote 4 - Dr. Karon Mclean (in-person)

Keynote Speakers

Zakia Hammal

Zakia Hammal
Carnegie Mellon University


Teodor Grantcherov

Teodor Grantcharov
Stanford University


Tanveer Syeda-Mahmood

Tanveer Syeda-Mahmood
IBM Research


Organizing Committee


Ehsan Adeli
Ehsan Adeli
Stanford University
Babak Taati
Babak Taati
Toronto Rehab Institute, University Health Network, & University of Toronto
James Cotton
R. James Cotton
Shirley Ryan AbilityLab
& Northwestern University
Itir Onal Ertugrul
Itir Önal Ertuğrul
Utrecht University






Advising/Steering Committee

Fei-Fei Li
Fei-Fei Li
Stanford University
James Landay
James Landay
Stanford University
Animashree (Anima) Anandkumar
Anima Anandkumar
NVIDIA & California Institute of Technology
Jeff Cohn
Jeff Cohn
University of Pittsburgh & Carnegie Mellon University
Nadia Berthouze
Nadia Berthouze
University College London

Program Committee

  • Alan Luo, Stanford University, USA
  • Ali Abedi, University Health Network, Toronto, Canada
  • Caroline Malin-Mayor, University of Toronto, Canada
  • Diego Guarin, University of Florida, USA
  • Jose Zariffa, University Health Network, Toronto, Canada
  • Maneesh Bilalpur, University of Pittsburgh, USA
  • Saurabh Hinduja, University of Pittsburgh, USA
  • Shrinidhi Kowshika Lakshmikanth, Stanford University, USA
  • Soroush Mehraban, University of Toronto, Canada
  • Vida Adeli, University of Toronto
  • Zane Durante, Stanford University, USA


Key Dates

  • Full Paper Deadline: June 25, 2023 23:59PM Pacific Time July 3, 2023 23:59PM Pacific Time (Extended)
  • Notification of Acceptance: July 16, 2023 
  • Camera-Ready Deadline: July 25, 2023 
  • Workshop Date: October 8, 2023


  • Papers will be limited to a maximum of 12 pages including references.
  • Formatting requirements are same as the main MICCAI conference. Please refer to MICCAI paper submission guidelines and FAQ pages.
  • Papers should be submitted electronically following the guidelines for authors and LaTeX and MS Word templates available at Lecture Notes in Computer Science, double blind review). No modifications to the templates are permitted.

Review Process

Submissions will undergo a double-blinded review process. All submissions will be peer-reviewed by 3 members of the program committee. The selection of the papers will be based on methodological novelty, contribution to the state-of-the-art, the significance of results, relevance, and clarity of presentation. 

Accepted Papers