Investigating the spectral and electrochemical properties of novel flavin-pyrene dyads separated via variable spacer.

The present manuscript describes the synthesis and the photophysical properties of a pair of novel flavin-pyrene dyads where the donor and the acceptor entities are separated via variable spacer. The dyads were well characterized using standard techniques and investigated for their photophysical and electrochemical nature. The observed absorption spectra of the dyads mainly display peaks corresponding to the individual pyrene and flavin units, with some contribution from the flavin entity in the pyrene region. While, strong emission quenching was observed for both the dyads if compared to its individual constituents. However, a careful analysis of the emission spectra and the solvent dependent studies reveals subtle difference between the two dyads. While no significant difference could be observed when excited in the flavin region; excitation at the pyrene region displays a weak and broad emission band in case of closely connected dyad. Further, the electrochemical properties were investigated by cyclic voltammetry and the reduction ability was observed to follow the trend as FlPy2 < FlPy1 < Fl.

Eigen Posture Based Fall Risk Assessment System Using Kinect.

Postural Instability (PI) is a major reason for fall in geriatric population as well as for people with diseases or disorders like Parkinson's, stroke etc. Conventional stability indicators like Berg Balance Scale (BBS) require clinical settings with skilled personnel's interventions to detect PI and finally classify the person into low, mid or high fall risk categories. Moreover these tests demand a number of functional tasks to be performed by the patient for proper assessment. In this paper a machine learning based approach is developed to determine fall risk with minimal human intervention using only Single Limb Stance exercise. The analysis is done based on the spatiotemporal dynamics of skeleton joint positions obtained from Kinect sensor. A novel posture modeling method has been applied for feature extraction along with some traditional time domain and metadata features to successfully predict the fall risk category. The proposed unobstrusive, affordable system is tested over 224 subjects and is able to achieve 75% mean accuracy on the geriatric and patient population.

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.

Analysis of muscle activation in lower extremity for static balance.

Balance plays an important role for human bipedal locomotion. Degeneration of balance control is prominent in stroke patients, elderly adults and even for majority of obese people. Design of personalized balance training program, in order to strengthen muscles, requires the analysis of muscle activation during an activity. In this paper we have proposed an affordable and portable approach to analyze the relationship between the static balance strategy and activation of various lower extremity muscles. To do that we have considered Microsoft Kinect XBox 360 as a motion sensing device and Wii balance board for measuring external force information. For analyzing the muscle activation pattern related to static balance, participants are asked to do the single limb stance (SLS) exercise on the balance board and in front of the Kinect. Static optimization to minimize the overall muscle activation pattern is carried out using OpenSim, which is an open-source musculoskeletal simulation software. The study is done on ten normal and ten obese people, grouped according to body mass index (BMI). Results suggest that the lower extremity muscles like biceps femoris, psoas major, sartorius, iliacus play the major role for both maintaining the balance using one limb as well as maintaining the flexion of the other limb during SLS. Further investigations reveal that the higher muscle activations of the flexed leg for normal group demonstrate higher strength. Moreover, the lower muscle activation of the standing leg for normal group demonstrate more headroom for the biceps femoris-short-head and psoas major to withstand the load and hence have better static balance control.

Affordable sensor based gaze tracking for realistic psychological assessment.

Eye movement analysis finds tremendous usefulness in various medical screening applications and rehabilitation. Infrared sensor based eye trackers are becoming popular but these are expensive and need repeated calibration. Moreover, with multiple calibration also, there persists some noises called, variable and systematic, resulting in inaccurate gaze tracking. This study aims to build an one time calibration module to avoid the overhead of multiple calibration and to design an algorithm to remove both the types of errors effectively. The proposed approach is used for correcting the gaze tracking data for Digit Gazing task and standard recall-recognition test, where an accuracy of 90% and 82% are achieved respectively for detecting the gaze positions against the raw eye gaze data. Results also show that it is possible to perform accurate gaze tracking with one-time calibration method provided the experimental setup is not altered.