Kingshuk Chakravarty

Person identification from arbitrary position and posture using kinect

In this paper authors have proposed a person identification method independent of his position with respect to the input sensor. The proposed method works for various postures or states namely, standing, sitting, walking. This method initially identifies the persons state and separate SVM based models are used for person identification (PI) for each of these three above mentioned states.

Unobtrusive indoor surveillance of patients at home using multiple Kinect sensors

In this paper we propose a system for unobtrusive automated indoor surveillance of subjects in indoor environment using the Kinect sensor. We demonstrate that the features of identity, location and activity of a person can be detected with considerable accuracy using the system. Further, we show how existing design patterns can be used to create a data parallel and scalable architecture for such surveillance in real-time.

Recognition of who is doing what from the stick model obtained from Kinect

In this demo, authors are going to demonstrate a method of identifying the person and his/her activities such as sitting, standing and walking using the skeleton information (stick model) obtained from Kinect. This set up is deployed in a drawing room for the real-time Television Rating Point (TRP) measurement.

Enhancing the usability of low-cost eye trackers for rehabilitation applications

Eye tracking is one of the most widely used technique for assessment, screening and human-machine interaction related applications. There are certain issues which limit the usage of eye trackers in practical scenarios, viz., i) need to perform multiple calibrations and ii) presence of inherent noise in the recorded data. To address these issues, we have proposed a protocol for one-time calibration against the “regular” or the “multiple” calibration phases. It is seen that though it is always desirable to perform multiple calibration, the one-time calibration also produces comparable results and might be better for individuals who are not able to perform multiple calibrations. In that case, “One-time calibration” can also be done by a participant and the calibration results are used for the rest of the participants, provided the chin rest and the eye tracker positions are unaltered. The second major issue is the presence of the inherent noise in the raw gaze data, leading to systematic and variable errors. We have proposed a signal processing chain to remove these two types of errors. Two different psychological stimuli-based tasks, namely, recall-recognition test and number gazing task are used as a case study for the same. It is seen that the proposed approach gives satisfactory results even with one-time calibration. The study is also extended to test the effect of long duration task on the performance of the proposed algorithm and the results confirm that the proposed methods work well in such scenarios too.

Wavelet based head movement artifact removal from electrooculography signals

Electrooculography (EOG) signals acquire different types of eye movements, which can be employed for human-machine interfaces (HMI) and also for diagnostic purposes. In realistic circumstances, EOG signals tend to be contaminated with noise due to unconstrained head movements. This noise degrades the signal quality as well as increases the misclassification rate of eye movement detection. General filtering and preprocessing techniques are unable to remove this noise. This paper presents a novel approach of head-movement noise removal from EOG signals by employing a biorthogonal wavelet transform to extract the level-4 approximation coefficients, which are also exploited as features classified by k- nearest neighbor (kNN) classifier. This approach enhances the classification performance remarkably. Even when this wavelet based technique is applied as denoising technique and features to the prior arts, it improves the performance of those existing techniques too. Moreover, the proposed technique is suitable for real time applications.