"Not one word of it made any sense" : Hyperbolic Synecdoche in the British National Corpus

A distinct metonymic pattern was discovered in the course of conducting a corpus-based study of figurative uses of WORD. The pattern involved examples such as Not one word of it made any sense and I agree with every word. It was labelled ‘hyperbolic synecdoche’, defined as a case in which a lexeme which typically refers to part of an entity (a) is used to stand for the whole entity and (b) is described with reference to the end point on a scale. Specifically, the speaker/writer selects the perspective of a lower-level unit (such as word for ‘utterance’), which is quantified as NOTHING or ALL, thus forming a subset of ‘extreme case formulations’. Hyperbolic synecdoche was found to exhibit a restricted range of lexicogrammatical patterns involving word, with the negated NOTHING patterns being considerably more common than the ALL patterns. The phenomenon was shown to be common in metonymic uses in general, constituting one-fifth of all cases of metonymy in word. The examples of hyperbolic synecdoche were found not to be covered by the oftquoted ‘abbreviation’ rationale for metonymy; instead, they represent a more roundabout way of expression. It is shown that other cases of hyperbolic synecdoche exist outside of word and the domain of communication (such as ‘time’ and ‘money’).

Motion Cues Analysis for Parkinson Gait Recognition

Background: Previous assessment methods for PG recognition used sensor mechanisms for PG that may cause discomfort. In order to avoid stress of applying wearable sensors, computer vision (CV) based diagnostic systems for PG recognition have been proposed. Main constraints in these methods are the laboratory setup procedures: Novel colored dresses for the patients were specifically designed to segment the test body from a specific colored background. Objective: To develop an image processing tool for home-assessment of Parkinson Gait(PG) by analyzing motion cues extracted during the gait cycles. Methods: The system is based on the idea that a normal body attains equilibrium during the gait by aligning the body posture with the axis of gravity. Due to the rigidity in muscular tone, persons with PD fail to align their bodies with the axis of gravity. The leaned posture of PD patients appears to fall forward. Whereas a normal posture exhibits a constant erect posture throughout the gait. Patients with PD walk with shortened stride angle (less than 15 degrees on average) with high variability in the stride frequency. Whereas a normal gait exhibits a constant stride frequency with an average stride angle of 45 degrees. In order to analyze PG, levodopa-responsive patients and normal controls were videotaped with several gait cycles. First, the test body is segmented in each frame of the gait video based on the pixel contrast from the background to form a silhouette. Next, the center of gravity of this silhouette is calculated. This silhouette is further skeletonized from the video frames to extract the motion cues. Two motion cues were stride frequency based on the cyclic leg motion and the lean frequency based on the angle between the leaned torso tangent and the axis of gravity. The differences in the peaks in stride and lean frequencies between PG and normal gait are calculated using Cosine Similarity measurements. Results: High cosine dissimilarity was observed in the stride and lean frequencies between PG and normal gait. High variations are found in the stride intervals of PG whereas constant stride intervals are found in the normal gait. Conclusions: We propose an algorithm as a source to eliminate laboratory constraints and discomfort during PG analysis. Installing this tool in a home computer with a webcam allows assessment of gait in the home environment.