Summary
Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Methodologies, Models and Algorithms for Patients Rehabilitation”. Objectives: This paper presents a camera based method for identifying the patient and detecting
interactions between the patient and the therapist during therapy. Detecting interactions
helps to discriminate between active and passive motion of the patient as well as
to estimate the accuracy of the skeletal data. Methods: Continuous face recognition is used to detect, recognize and track the patient with
other people in the scene (e.g. the therapist, or a clinician). We use a method based
on local binary patterns (LBP). After identifying users in the scene we identify interactions
between the patient and other people. We use a depth map/point cloud for estimating
the distance between two people. Our method uses the association of depth regions
to user identities and computes the minimal distance between the regions. Results: Our results show state-of-the-art performance of real-time face recognition using
low-resolution images that is sufficient to use in adaptive systems. Our proposed
approach for detecting interactions shows 91.9% overall recognition accuracy what
is sufficient for applications in the context of serious games. We also discuss limitations
of the proposed method as well as general limitations of using depth cameras for serious
games. Conclusions: We introduced a new method for frame-by-frame automated identification of the patient
and labeling reliable sequences of the patient’s data recorded during rehabilitation
(games). Our method improves automated rehabilitation systems by detecting the identity
of the patient as well as of the therapist and by detecting the distance between both
over time.
Keywords
User identification - interaction detection - rehabilitation - face recognition
