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.

Disorganization and timing of motor behavior : insight from gesture impairments and movement patterns in schizophrenia

Background: Motor symptoms are frequent phenomena across the entire course of schizophrenia1. Some have argued that disorganized behavior was associated with aberrant motor behavior. We have studied the association of motor disturbances and disorganization in two projects focusing on the timing of movements. Method: In two studies, we assessed motor behavior and psychopathology. The first study applied a validated test of upper limb apraxia in 30 schizophrenia patients2,3. We used standardized video assessments of hand gestures by a blinded rater. The second study tested the stability of movement patterns using time series analysis in actigraphy data of 100 schizophrenia patients4. Both stability of movement patterns and the overall amount of movement were calculated from data of two hours with high degrees of social interaction comparable across the 100 subjects. Results: In total, 67% of the patients had gesture performance deficits3. Most frequently, they made spatial, temporal and body-part-as-object errors. Gesture performance relied on frontal lobe function2. Poor gesture performance was associated with increased disorganization scores. In the second study, we found disorganization to be predicted only by more irregular movement patterns irrespective of the overall amount of movement4. Conclusion : Both studies provide evidence for a link between aberrant timing of motor behavior and disorganization. Disturbed movement control seems critical for disorganized behavior in schizophrenia.

Nonverbal social communication and gesture control in schizophrenia

Schizophrenia patients are severely impaired in nonverbal communication, including social perception and gesture production. However, the impact of nonverbal social perception on gestural behavior remains unknown, as is the contribution of negative symptoms, working memory, and abnormal motor behavior. Thus, the study tested whether poor nonverbal social perception was related to impaired gesture performance, gestural knowledge, or motor abnormalities. Forty-six patients with schizophrenia (80%), schizophreniform (15%), or schizoaffective disorder (5%) and 44 healthy controls matched for age, gender, and education were included. Participants completed 4 tasks on nonverbal communication including nonverbal social perception, gesture performance, gesture recognition, and tool use. In addition, they underwent comprehensive clinical and motor assessments. Patients presented impaired nonverbal communication in all tasks compared with controls. Furthermore, in contrast to controls, performance in patients was highly correlated between tasks, not explained by supramodal cognitive deficits such as working memory. Schizophrenia patients with impaired gesture performance also demonstrated poor nonverbal social perception, gestural knowledge, and tool use. Importantly, motor/frontal abnormalities negatively mediated the strong association between nonverbal social perception and gesture performance. The factors negative symptoms and antipsychotic dosage were unrelated to the nonverbal tasks. The study confirmed a generalized nonverbal communication deficit in schizophrenia. Specifically, the findings suggested that nonverbal social perception in schizophrenia has a relevant impact on gestural impairment beyond the negative influence of motor/frontal abnormalities.

Structural brain correlates of defective gesture performance in schizophrenia

INTRODUCTION The neural correlates of impaired performance of gestures are currently unclear. Lesion studies showed variable involvement of the ventro-dorsal stream particularly left inferior frontal gyrus (IFG) in gesture performance on command. However, findings cannot be easily generalized as lesions may be biased by the architecture of vascular supply and involve brain areas beyond the critical region. The neuropsychiatric syndrome of schizophrenia shares apraxic-like errors and altered brain structure without macroanatomic lesions. Schizophrenia may therefore qualify as a model disorder to test neural correlates of gesture impairments. METHODS We included 45 schizophrenia patients and 44 healthy controls in the study to investigate the structural brain correlates of defective gesturing in schizophrenia using voxel based morphometry. Gestures were tested in two domains: meaningful gestures (transitive and intransitive) on verbal command and imitation of meaningless gestures. Cut-off scores were used to separate patients with deficits, patients without deficits and controls. Group differences in gray matter (GM) volume were explored in an ANCOVA. RESULTS Patients performed poorer than controls in each gesture category (p < .001). Patients with deficits in producing meaningful gestures on command had reduced GM predominantly in left IFG, with additional involvement of right insula and anterior cingulate cortex. Patients with deficits differed from patients without deficits in right insula, inferior parietal lobe (IPL) and superior temporal gyrus. CONCLUSIONS Impaired performance of meaningful gestures on command was linked to volume loss predominantly in the praxis network in schizophrenia. Thus, the behavioral similarities between apraxia and schizophrenia are paralleled by structural alterations. However, few associations between behavioral impairment and structural brain alterations appear specific to schizophrenia.

User-independent accelerometer-based gesture recognition for mobile devices

Many mobile devices embed nowadays inertial sensors. This enables new forms of human-computer interaction through the use of gestures (movements performed with the mobile device) as a way of communication. This paper presents an accelerometer-based gesture recognition system for mobile devices which is able to recognize a collection of 10 different hand gestures. The system was conceived to be light and to operate in a user -independent manner in real time. The recognition system was implemented in a smart phone and evaluated through a collection of user tests, which showed a recognition accuracy similar to other state-of-the art techniques and a lower computational complexity. The system was also used to build a human -robot interface that enables controlling a wheeled robot with the gestures made with the mobile phone.

Authentication in mobile devices through hand gesture recognition

This article proposes an innovative biometric technique based on the idea of authenticating a person on a mobile device by gesture recognition. To accomplish this aim, a user is prompted to be recognized by a gesture he/she performs moving his/her hand while holding a mobile device with an accelerometer embedded. As users are not able to repeat a gesture exactly in the air, an algorithm based on sequence alignment is developed to correct slight differences between repetitions of the same gesture. The robustness of this biometric technique has been studied within 2 different tests analyzing a database of 100 users with real falsifications. Equal Error Rates of 2.01 and 4.82% have been obtained in a zero-effort and an active impostor attack, respectively. A permanence evaluation is also presented from the analysis of the repetition of the gestures of 25 users in 10 sessions over a month. Furthermore, two different gesture databases have been developed: one made up of 100 genuine identifying 3-D hand gestures and 3 impostors trying to falsify each of them and another with 25 volunteers repeating their identifying 3- D hand gesture in 10 sessions over a month. These databases are the most extensive in published studies, to the best of our knowledge.