Computer Aided Written Character Feature Extraction in Progressive Supranuclear Palsy and Parkinson's Disease.

Parkinson's disease (PD) and progressive supranuclear palsy (PSP) are neurodegenerative movement disorders associated with cognitive dysfunction. The Luria's Alternating Series Test (LAST) is a clinical tool sensitive to both graphomotor problems and perseverative tendencies that may suggest the dysfunction of prefrontal and/or frontostriatal areas and may be used in PD and PSP assessment. It requires the participant to draw a series of alternating triangles and rectangles. In the study, two clinical groups-51 patients with PD and 22 patients with PSP-were compared to 32 neurologically intact seniors. Participants underwent neuropsychological assessment. The LAST was administered in a paper and pencil version, then scanned and preprocessed. The series was automatically divided into characters, and the shapes were recognized as rectangles or triangles. In the feature extraction step, each rectangle and triangle was regarded both as an image and a two-dimensional signal, separately and as a part of the series. Standard and novel features were extracted and normalized using characters written by the examiner. Out of 71 proposed features, 51 differentiated the groups (p < 0.05). A classifier showed an accuracy of 70.5% for distinguishing three groups.

Video-based time assessment in 360 degrees turn Berg balance test.

The Great Geriatric Problems include dementia syndromes, locomotion disorders, and falls. In geriatrics, one of the most commonly used methods for assessing the balance of seniors is the Berg Balance Scale (BBS). It is a set of 14 exercises that reflect daily activates. In this paper focused on the 11th Berg Balance Test, a novel method is introduced, based on the time of rotation calculated using the CamShift algorithm. The method is tested on 57 recordings of seniors. The average relative error of presented method is 4.74%, which refers to the average absolute error at the level of 0.83 s.

Leg movement tracking in automatic video-based one-leg stance evaluation.

Falls are a major risk in elder population. Early diagnosis is therefore an important step towards increasing the safety of elders. One of the common diagnostic tests is the Berg Balance Scale (BBS), consisting of 14 exercises arranged from the easiest (sitting-to-standing) to the most demanding (one-leg stance). In this study a novel approach to the automatic assessment of the time in which the patient can remain in the one-leg stance position without loosing balance is introduced. The data is collected using a regular video camera. No markers, special garments, or system calibration are required. Two groups are examined. The first group consists of 16 students: healthy, young adults (12 female, 4 male, avg. 20yrs±1). The second group consists of 50 elders (39 female, 11 male, avg. 78.8yrs±5.9). Data (short, one minute recordings) are collected in a controlled environment using a digital video recorder (50fps, 1920×1080) fixed to a tripod. Data are processed off-line. First, the region of interest is determined. Next, the Kanade-Lucas-Tomasi tracking is performed. Best tracks are selected based on the registered vertical movement and two tracks are obtained corresponding to the left and right leg movements. Tracks are later subjected to the sparse signal baseline estimation, denoising and thresholding to detect the raised leg. Results are compared frame-wise to the ground truth reference obtained in the manual processing procedure. Both legs are evaluated in the elder group (in all cases several attempts featuring both legs were registered), resulting in 89.18%±11.27% DICE, 93.07%±5.43% sensitivity and 96.94%±6.11% specificity values for both legs. The signal of a single leg is evaluated in the student group (in all cases only one attempt was needed to perform the full examination) resulting in 98.96%±1.2% DICE, 98.78%±1.65% sensitivity and 98.73%±2.69% specificity values. This is the first step towards a video-based system enabling the automatic assessment of the four last, most vital tasks of the Berg Balance Scale evaluation.

Spatial and dynamical handwriting analysis in mild cognitive impairment.

Background and Objectives Standard clinical procedure of Mild Cognitive Impairment (MCI) assessment employs time-consuming tests of psychological evaluation and requires the involvement of specialists. The employment of quantitative methods proves to be superior to clinical judgment, yet reliable, fast and inexpensive tests are not available. This study was conducted as a first step towards the development of a diagnostic tool based on handwriting. Methods In this paper the handwriting sample of a group of 37 patients with MCI (mean age 76.1±5.8) and 37 healthy controls (mean age 74.8±5.7) was collected using a Livescribe Echo Pen while completing three tasks: (1) regular writing, (2) all-capital-letters writing, and (3) single letter multiply repeated. Parameters differentiating both groups were selected in each task. Results Subjects with confirmed MCI needed more time to complete task one (median 119.5s, IQR - interquartile range - 38.1 vs. 95.1s, IQR 29.2 in control and MCI group, p-value <0.05) and two (median 84.2s, IQR 49.2 and 53.7s, IQR 30.5 in control and MCI group) as their writing was significantly slower. These results were associated with a longer time to complete a single stroke of written text. The written text was also noticeably larger in the MCI group in all three tasks (e.g. median height of the text block in task 2 being 22.3mm, IQR 12.9 in MCI and 20.2mm, IQR 8.7 in control group). Moreover, the MCI group showed more variation in the dynamics of writing: longer pause between strokes in task 1 and 2. The all-capital-letters task produced most of the discriminating features. Conclusion Proposed handwriting features are significant in distinguishing MCI patients. Inclusion of quantitative handwriting analysis in psychological assessment may be a step forward towards a fast MCI diagnosis.