Comprehensive assessment of gesture production: a new test of upper limb apraxia (TULIA)

BACKGROUND: Only few standardized apraxia scales are available and they do not cover all domains and semantic features of gesture production. Therefore, the objective of the present study was to evaluate the reliability and validity of a newly developed test of upper limb apraxia (TULIA), which is comprehensive and still short to administer. METHODS: The TULIA consists of 48 items including imitation and pantomime domain of non-symbolic (meaningless), intransitive (communicative) and transitive (tool related) gestures corresponding to 6 subtests. A 6-point scoring method (0-5) was used (score range 0-240). Performance was assessed by blinded raters based on videos in 133 stroke patients, 84 with left hemisphere damage (LHD) and 49 with right hemisphere damage (RHD), as well as 50 healthy subjects (HS). RESULTS: The clinimetric findings demonstrated mostly good to excellent internal consistency, inter- and intra-rater (test-retest) reliability, both at the level of the six subtests and at individual item level. Criterion validity was evaluated by confirming hypotheses based on the literature. Construct validity was demonstrated by a high correlation (r = 0.82) with the De Renzi-test. CONCLUSION: These results show that the TULIA is both a reliable and valid test to systematically assess gesture production. The test can be easily applied and is therefore useful for both research purposes and clinical practice.

Interference with gesture production by theta burst stimulation over left inferior frontal cortex

The traditional view of a predominant inferior parietal representation of gestures has been recently challenged by neuroimaging studies demonstrating that gesture production and discrimination may critically depend on inferior frontal lobe function. The aim of the present work was therefore to investigate the effect of transient disruption of these brain sites by continuous theta burst stimulation (cTBS) on gesture production and recognition.

Impaired gesture performance in schizophrenia: particular vulnerability of meaningless pantomimes

Schizophrenia patients frequently present with subtle motor impairments, including higher order motor function such as hand gesture performance. Using cut off scores from a standardized gesture test, we previously reported gesture deficits in 40% of schizophrenia patients irrespective of the gesture content. However, these findings were based on normative data from an older control group. Hence, we now aimed at determining cut-off scores in an age and gender matched control group. Furthermore, we wanted to explore whether gesture categories are differentially affected in Schizophrenia. Gesture performance data of 30 schizophrenia patients and data from 30 matched controls were compared. Categories included meaningless, intransitive (communicative) and transitive (object related) hand gestures, which were either imitated or pantomimed, i.e. produced on verbal command. Cut-off scores of the age matched control group were higher than the previous cut-off scores in an older control group. An ANOVA tested effects of group, domain (imitation or pantomime), and semantic category (meaningless, transitive or intransitive), as well as their interaction. According to the new cut-off scores, 67% of the schizophrenia patients demonstrated gestural deficits. Patients performed worse in all gesture categories, however meaningless gestures on verbal command were particularly impaired (p = 0.008). This category correlated with poor frontal lobe function (p < 0.001). In conclusion, gestural deficits in schizophrenia are even more frequent than previously reported. Gesture categories that pose higher demands on planning and selection such as pantomime of meaningless gestures are predominantly affected and associated with the well-known frontal lobe dysfunction.

Sparse representation based action and gesture recognition

In this paper we present a solution to the problem of action and gesture recognition using sparse representations. The dictionary is modelled as a simple concatenation of features computed for each action or gesture class from the training data, and test data is classified by finding sparse representation of the test video features over this dictionary. Our method does not impose any explicit training procedure on the dictionary. We experiment our model with two kinds of features, by projecting (i) Gait Energy Images (GEIs) and (ii) Motion-descriptors, to a lower dimension using Random projection. Experiments have shown 100% recognition rate on standard datasets and are compared to the results obtained with widely used SVM classifier.