Activity recognition in adaptive assistive systems using artificial neural networks

K

Master of Science

thesisComputing and data acquisition have become an integral part of everyday life. From reading emails on cell phones to kids playing with motion sensing game consoles, we are surrounded with sensors and mobile computing devices. As the availability of powerful computing devices increases, applications in previously limited environments become possible. Training devices in rehabilitation are becoming increasingly common and more mobile. Community based rehabilitative devices are emerging that embrace these mobile advances. To further the flexibility of devices used in rehabilitation, research has explored the use of smartphones as a means to process data and provide feedback to the user. In combination with sensor embedded insoles, smartphones provide a powerful tool for the clinician in gathering data and as a standalone training tool in rehabilitation. This thesis presents the continuing research of sensor based insoles, feedback systems and increasing the capabilities of the Adaptive Real-Time Instrumentation System for Tread Imbalance Correction, or ARTISTIC, with the introduction of ARTISTIC 2.0. To increase the capabilities of the ARTISTIC an Inertial Measurement Unit (IMU) was added, which gave the system the ability to quantify the motion of the gait cycle and, more specifically, determine stride length. The number of sensors in the insole was increased from two to ten, as well as placing the microprocessor and a vibratory motor in the insole. The transmission box weight was reduced by over 50 percent and the volume by over 60 percent. Stride length was validated against a motion capture system and found the average stride length to be within 2.7 ± 6.9 percent. To continue the improvement of the ARTISTIC 2.0, future work will include implementing real-time stride length feedback

Breathing Monitoring and Pattern Recognition with Wearable Sensors

This chapter introduces the anatomy and physiology of the respiratory system, and the reasons for measuring breathing events, particularly, using wearable sensors. Respiratory monitoring is vital including detection of sleep apnea and measurement of respiratory rate. The automatic detection of breathing patterns is equally important in other respiratory rehabilitation therapies, for example, magnetic resonance exams for respiratory triggered imaging, and synchronized functional electrical stimulation. In this context, the goal of many research groups is to create wearable devices able to monitor breathing activity continuously, under natural physiological conditions in different environments. Therefore, wearable sensors that have been used recently as well as the main signal processing methods for breathing analysis are discussed. The following sensor technologies are presented: acoustic, resistive, inductive, humidity, acceleration, pressure, electromyography, impedance, and infrared. New technologies open the door to future methods of noninvasive breathing analysis using wearable sensors associated with machine learning techniques for pattern detection

Master of Science

thesisComputing and data acquisition have become an integral part of everyday life. From reading emails on a cell phone, to kids playing with motion sensing game consoles, we are surrounded with sensors and mobile devices. As the availability of powerful mobile computing devices expands, the road is paved for applications in previously limited environments. Rehabilitative devices are emerging that embrace these mobile advances. Research has explored the use of smartphones in rehabilitation as a means to process data and provide feedback in conjunction with established rehabilitative methods. Smartphones, combined with sensor embedded insoles, provide a powerful tool for the clinician in gathering data and may act as a standalone training technique. This thesis presents continuing research of a sensor integrated insole system that provides real-time feedback through a mobile platform, the Adaptive Real-Time Instrumentation System for Tread Imbalance Correction (ARTISTIC). The system interfaces a wireless instrumented insole with an Android smartphone application to receive gait data and provide sensory feedback to modify gait patterns. Revisions to the system hardware, software, and feedback modes brought about the introduction of the ARTISTIC 2.0. The number of sensors in the insole was increased from two to 10. The microprocessor and a vibrotactile motor were embedded in the insole and the communications box was reduced in size and weight by more than 50%. Stance time iv measurements were validated against force plate equipment and found to be within 13.5 ± 3.3% error of force plate time measurements. Human subjects were tested using each of the feedback modes to alter gait symmetry. Results from the testing showed that more than one mode of feedback caused a statistically significant change in gait symmetry ratios (p < 0.05). Preference of feedback modes varied among subjects, with the majority agreeing that several feedback modes made a difference in their gait. Further improvements will prepare the ARTISTIC 2.0 for testing in a home environment for extended periods of time and improve data capture techniques, such as including a database in the smartphone application

Doctor of Philosophy

dissertationMotion capture has applications in many fields. A need has arisen for motion capture systems that are low-cost, mobile, and intuitive. An attitude heading reference system (AHRS) calculates the global orientation of a rigid body by synthesizing the output from an array of sensors. A complete motion capture system utilizing gyroscopes, accelerometers, and magnetometers attached to the main body segments of a human is proposed. This is accomplished by providind a low-cost calibration procedure for micro electro-mechanical system (MEMS) gyroscopes, accelerometers, and magnetometers in order to create a custom AHRS unit. The accuracy of reproducing global orientations using these AHRS units is analyzed for individual modules as well as redundant groups of AHRS nodes for increased accuracy. In order to make the system intuitive, a localization procedure for finding the locations of all AHRS units attached to the body is proposed. Sensors were successfully calibrated to an accuracy sufficient for AHRS development. The accuracy of the AHRS units was verified and led to a functioning motion capture system. The localization procedure was verified with volunteer subjects and successfully finds the location of all attached AHRS units

Usable Security for Wireless Body-Area Networks

We expect wireless body-area networks of pervasive wearable devices will enable in situ health monitoring, personal assistance, entertainment personalization, and home automation. As these devices become ubiquitous, we also expect them to interoperate. That is, instead of closed, end-to-end body-worn sensing systems, we envision standardized sensors that wirelessly communicate their data to a device many people already carry today, the smart phone. However, this ubiquity of wireless sensors combined with the characteristics they sense present many security and privacy problems. In this thesis we describe solutions to two of these problems. First, we evaluate the use of bioimpedance for recognizing who is wearing these wireless sensors and show that bioimpedance is a feasible biometric. Second, we investigate the use of accelerometers for verifying whether two of these wireless sensors are on the same person and show that our method is successful as distinguishing between sensors on the same body and on different bodies. We stress that any solution to these problems must be usable, meaning the user should not have to do anything but attach the sensor to their body and have them just work. These methods solve interesting problems in their own right, but it is the combination of these methods that shows their true power. Combined together they allow a network of wireless sensors to cooperate and determine whom they are sensing even though only one of the wireless sensors might be able to determine this fact. If all the wireless sensors know they are on the same body as each other and one of them knows which person it is on, then they can each exploit the transitive relationship to know that they must all be on that person’s body. We show how these methods can work together in a prototype system. This ability to operate unobtrusively, collecting in situ data and labeling it properly without interrupting the wearer’s activities of daily life, will be vital to the success of these wireless sensors