• A smartphone camera reveals an ‘invisible’ Parkinsonian tremor: a potential pre-motor biomarker?

      Williams, S.; Fang, H.; Alty, J.; Qahwaji, Rami S.R.; Patel, P.; Graham, C.D. (2018)
      There are a wide variety of ways to objectively detect neurological signs, but these either require special hard-ware (such as wearable technology) or patient behaviour change (such as engagement with smartphone tasks) [2]. Neither constraint applies to the technology of computer vision, which is the processing of single or multiple camera images by computer to automatically derive useful information. The only equipment involved is ubiquitous: camera and computer.We report a computer vision-enhanced video sequence from a 68-year-old man, diagnosed with idiopathic Parkinson’s disease 2 years previously.
    • Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos

      Williams, S.; Relton, S.D.; Fang, H.; Alty, J.; Qahwaji, Rami S.R.; Graham, C.D.; Wong, D.C. (2020-11)
      Background: Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best. Aim: We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia. Methods: We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0–1) or mild/moderate/severe bradykinesia (UPDRS = 2–4), and presence or absence of Parkinson's diagnosis. Results: A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67. Conclusion: The method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.
    • Video Indexing and Retrieval in Compressed Domain Using Fuzzy-Categorization.

      Fang, H.; Qahwaji, Rami S.R.; Jiang, Jianmin (2006)
      There has been an increased interest in video indexing and retrieval in recent years. In this work, indexing and retrieval system of the visual contents is based on feature extracted from the compressed domain. Direct possessing of the compressed domain spares the decoding time, which is extremely important when indexing large number of multimedia archives. A fuzzy-categorizing structure is designed in this paper to improve the retrieval performance. In our experiment, a database that consists of basketball videos has been constructed for our study. This database includes three categories: full-court match, penalty and close-up. First, spatial and temporal feature extraction is applied to train the fuzzy membership functions using the minimum entropy optimal algorithm. Then, the max composition operation is used to generate a new fuzzy feature to represent the content of the shots. Finally, the fuzzy-based representation becomes the indexing feature for the content-based video retrieval system. The experimental results show that the proposal algorithm is quite promising for semantic-based video retrieval.