CuPiD

EU FP7-ICT-2011-7 (grant number 288516)

People

Michael Hardegger

Sinziana Mazilu

This project is supervised by Dr Daniel Roggen and Prof Gerhard Tröster

Closed-loop system for personalized and at-home rehabilitation of people with Parkinson's Disease

People with Parkinson’s disease (PD) suffer from motor and cognitive impairments that severely impact mobility, fall risk, and multiple key aspects of functional independence.

Until recently, treatment goals focused almost exclusively on symptom relief, but exciting recent work by CuPiD partners and others has demonstrated that motor learning and rehabilitation principles can be effective even in the presence of PD. It is critical to make these rehab-like therapies accessible to patients in their home-setting since they need continuous training, as PD is a chronic neurodegenerative disease.

In CuPiD we develop an ICT-enabled solution to the rehabilitation of patients with PD in their home setting, tailoring the solution to target mobility, cognitive function and debilitating PD symptoms such as freezing of gait.

Our Objective: Wearable Technologies for Freezing of Gait Rehabilitation

Within CuPiD we develop a new system for a rehabilitation program that will be carried out in the at home environment and will be aimed to alleviate freezing of gait (FOG) in patients with Parkinson's disease (PD).

There are two main components to this rehabilitation program:

Both components form part of a larger tele-rehabilitation program at home, which is evaluated in an 18 month clinical evaluation. 

Showcase

Wearable Simultaneous Localization and Mapping

Other contributors

• Zack Zhu
  

Links

CuPiD Official Website

Publications

2013

• Engineers Meet Clinicians: Augmenting Parkinson's Disease Patients to Gather Information for Gait Rehabilitation
  Sinziana Mazilu, Eran Gazit, Ulf Blanke, Daniel Roggen, Jeffrey M Hausdorff and Gerhard Tröster
  in: Augmented Human International Conference in Cooperation with ACM SIGCHI (AH 2013), 2013
  [ BiBTeX RIS ]
• Feature Learning for Detection and Prediction of Freezing of Gait in Parkinson's Disease
  Sinziana Mazilu, Alberto Calatroni, Eran Gazit, Daniel Roggen, Jeffrey M Hausdorff and Gerhard Tröster
  in: MLDM, 2013
  [ BiBTeX RIS ]

2012

• ActionSLAM: Using location-related actions as landmarks in pedestrian SLAM
  Michael Hardegger, Daniel Roggen, Sinziana Mazilu and Gerhard Tröster
  in: Proceedings of the 3rd International Conference on Indoor Positioning and Indoor Navigation (IPIN) [Best Student Paper Award], Sydney, Australia, 2012
  [ BiBTeX RIS ]
• Online Detection of Freezing of Gait with Smartphones and Machine Learning Techniques
  Sinziana Mazilu, Michael Hardegger, Zack Zhu, Daniel Roggen, Gerhard Tröster, Meir Plotnik and Jeffrey M Hausdorff
  in: 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2012
  [ BiBTeX RIS ]
• Real-Time Detection of Freezing of Gait for Parkinson's Disease Patients via Smartphone
  Zack Zhu, Sinziana Mazilu, Michael Hardegger, Meir Plotnik, Jeffrey M Hausdorff, Daniel Roggen and Gerhard Tröster
  in: Adjunct Proceedings of the 10th International Conference on Pervasive Computing (Pervasive 2012), Newcastle, United Kingdom, 2012
  [ BiBTeX RIS PDF ]