Avinash Parnandi

Grip Control Using Biomimetic Tactile Sensing Systems

We present a proof-of-concept for controlling the grasp of an anthropomorphic mechatronic prosthetic hand by using a biomimetic tactile sensor, Bayesian inference, and simple algorithms for estimation and control. The sensor takes advantage of its compliant mechanics to provide a triaxial force sensing end-effector for grasp control. By calculating normal and shear forces at the fingertip, the prosthetic hand is able to maintain perturbed objects within the force cone to prevent slip. A Kalman filter is used as a noise-robust method to calculate tangential forces. Biologically inspired algorithms and heuristics are presented that can be implemented online to support rapid, reflexive adjustments of grip.

Motor function assessment using wearable inertial sensors

We present an approach to wearable sensor-based assessment of motor function in individuals post stroke. We make use of one on-body inertial measurement unit (IMU) to automate the functional ability (FA) scoring of the Wolf Motor Function Test (WMFT). WMFT is an assessment instrument used to determine the functional motor capabilities of individuals post stroke. It is comprised of 17 tasks, 15 of which are rated according to performance time and quality of motion. We present signal processing and machine learning tools to estimate the WMFT FA scores of the 15 tasks using IMU data. We treat this as a classification problem in multidimensional feature space and use a supervised learning approach.

Automated administration of the Wolf Motor Function Test for post-stroke assessment

The advent of new health sensing technologies has presented us with the opportunity to gain richer data from patients undergoing clinical interventions. Such technologies are particularly suited for applications requiring temporal accuracy. The Wolf Motor Function Test (WMFT) is one such application. This assessment is an instrument used to determine functional ability of the paretic and non-paretic limbs in individuals post-stroke. It consists of 17 tasks, 15 of which are scored according to both time and a functional ability scale. We propose a technique that uses wearable sensors and performance sensors to estimate the timing of seven of these tasks. We have developed a sensing framework and an algorithm to automatically detect total movement time. We have validated the systems accuracy on the seven selected WMFT tasks. We also suggest how this framework can be adapted to the remaining tasks.

Coarse In-building Localization with Smartphones ?

Geographic location of a person is important contextual information that can be used in a variety of scenarios like disaster relief, directional assistance, context-based advertisements, etc. GPS provides accurate localization outdoors but is not useful inside buildings. We propose an coarse indoor localization ap- proach that exploits the ubiquity of smart phones with embedded sensors. GPS is used to find the building in which the user is present. The Accelerometers are used to recognize the users dynamic activities (going up or down stairs or an elevator) to determine his/her location within the building. We demonstrate the ability to estimate the floor-level of a user. We compare two techniques for activ- ity classification, one is naive Bayes classifier and the other is based on dynamic time warping. The design and implementation of a localization application on the HTC G1 platform running Google Android is also presented.

A Control-Theoretic Approach to Adaptive Physiological Games

We present an adaptive biofeedback game that aims to maintain the players arousal level by monitoring physiological signals. We use concepts from control theory to model the interaction between human physiology and game difficulty during game play. We validate the approach on a car-racing game with real-time adaptive game mechanics. Specifically, we use car speed, road visibility, and steering jitter as three mechanisms to manipulate game difficulty. We propose quantitative measures to characterize the effectiveness of these game adaptations in manipulating the players arousal. For this purpose, we use electro dermal activity (EDA) as a physiological correlate of arousal. Experimental trials with 20 subjects in both open-loop (no feedback) and closed-loop (negative feedback) conditions show statistically significant differences among the three game mechanics in terms of their effectiveness. Specifically, manipulating car speed provides higher arousal levels than changing road visibility or vehicle steering. Finally, we discuss the theoretical and practical implications of our approach.

Using socially assistive robotics to augment motor task performance in individuals post-stroke

This paper presents an application of a socially assistive robotics (SAR) system to hands-off post-stroke rehabilitation. We validate the technical feasibility and efficacy of our system in guiding, motivating, and administering an upper extremity rehabilitation task. The robot, which consists of a humanoid torso on a mobile base, monitors user performance on a wire puzzle task through a wearable inertial measurement unit and signals from the puzzle. Smoothness of stroke-affected limb movement is used as the evaluation metric. Five adults of mild to moderate functional ability in the chronic phase of stroke recovery interacted with our SAR system over three separate days. The inertial data from the five participants were analyzed using frequency domain techniques. Subsequently, the amount of power in frequency bands corresponding to voluntary (0.1 to 2Hz) and involuntary motion/jerk (4 to 8Hz) was evaluated. We found that, in adults of mild severity (Upper Extremity Fugl-Meyer Assessment scores greater than 40), the motion becomes smoother (the amount of jerk is reduced) over 3 days of task practice. In adults of moderate motor severity (scores below 40), the motion became less smooth. This may indicate that the combination of our task and SAR system is better suited for individuals with higher functional ability, and needs augmentation in order to aid those of lower functional ability levels.

Chill-Out: Relaxation Training through Respiratory Biofeedback in a Mobile Casual Game

We present Chill-Out, an adaptive biofeedback game that teaches relaxation skills by monitoring the breathing rate of the player. The game uses a positive feedback loop that penalizes fast breathing by means of a proportional-derivative control law: rapid (and/or increasing) breathing rates increase game difficulty and reduce the final score of the game. We evaluated Chill-Out against a conventional non-biofeedback game and traditional relaxation based on deep breathing. Measurements of breathing rate, electrodermal activity, and heart rate variability show that playing Chill-Out leads to lower arousal during a subsequent task designed to induce stress.

Socially Assistive Robotics for Guiding Motor Task Practice

Due to their quantitative nature, robotic systems are useful tools for systematically augmenting human behavior and performance in dynamic environments, such as therapeutic rehabilitation settings. The efficacy of human-robot interaction (HRI) in these settings will depend on the robot’s coaching style. Our goal was to investigate the influence of robot coaching styles designed to enhance motivation and encouragement on post-stroke individuals during motor task practice. We hypothesized that coaching styles incorporating user performance and preference would be preferred in a therapeutic HRI setting. We designed an evaluation study with seven individuals post stroke. A socially assistive robotics (SAR) system using three different coaching styles guided participants during performance of an upper extremity practice task. User preference was not significantly affected by the different robot coaching styles in our participant sample (H(2) = 2.638, p = 0.267). However, trends indicated differences in preference for the coaching styles. Our results provide insights into the design and use of SAR systems in therapeutic interactions aiming to influence user behavior.

A comparative study of game mechanics and control laws for an adaptive physiological game

We present an adaptive biofeedback game that aims to maintain the player’s arousal by modifying game difficulty in response to the player’s physiological state, as measured with wearable sensors. Our approach models the interaction between human physiology and game difficulty during gameplay as a control problem, where game difficulty is the system input and player arousal its output. We validate the approach on a car-racing game with real-time adaptive game mechanics. Specifically, we use (1) car speed, road visibility, and steering jitter as three mechanisms to manipulate game difficulty, (2) electrodermal activity as physiological correlate of arousal, and (3) two types of control law: proportional (P) control, and proportional-integral-derivative (PID) control. We also propose quantitative measures to characterize the effectiveness of these game adaptations and controllers in manipulating the player’s arousal. Experimental trials with 25 subjects in both open-loop (no feedback) and closed-loop (negative feedback) conditions show statistically significant differences in effectiveness among the three game mechanics and also between the two control laws. Specifically, manipulating car speed provides higher control of arousal levels than changing road visibility or vehicle steering. Our results also confirm that PID control leads to lower error and reduced oscillations in the closed-loop response compared to proportional-only control. Finally, we discuss the theoretical and practical implications of our approach.

Architecture of an automated therapy tool for childhood apraxia of speech

We present a multi-tier system for the remote administration of speech therapy to children with apraxia of speech. The system uses a client-server architecture model and facilitates task-oriented remote therapeutic training in both in-home and clinical settings. Namely, the system allows a speech therapist to remotely assign speech production exercises to each child through a web interface, and the child to practice these exercises on a mobile device. The mobile app records the childs utterances and streams them to a back-end server for automated scoring by a speech-analysis engine. The therapist can then review the individual recordings and the automated scores through a web interface, provide feedback to the child, and adapt the training program as needed. We validated the system through a pilot study with children diagnosed with apraxia of speech, and their parents and speech therapists. Here we describe the overall client-server architecture, middleware tools used to build the system, the speech-analysis tools for automatic scoring of recorded utterances, and results from the pilot study. Our results support the feasibility of the system as a complement to traditional face-to-face therapy through the use of mobile tools and automated speech analysis algorithms.