Classifying Human Leg Motions with Uniaxial Piezoelectric Gyroscopes

This paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). A performance comparison of these classification techniques is provided in terms of their correct differentiation rates, confusion matrices, computational cost, and training and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that BDM, in general, results in the highest correct classification rate with relatively small computational cost

Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals

We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWT decomposition and reconstruction

Gait Analysis Using Wearable Sensors

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications

Gait Event Detection on Level Ground and Incline Walking Using a Rate Gyroscope

Gyroscopes have been proposed as sensors for ambulatory gait analysis and functional electrical stimulation systems. Accurate determination of the Initial Contact of the foot with the floor (IC) and the final contact or Foot Off (FO) on different terrains is important. This paper describes the evaluation of a gyroscope placed on the shank for determination of IC and FO in subjects walking outdoors on level ground, and up and down an incline. Performance was compared with a reference pressure measurement system. The mean difference between the gyroscope and the reference was less than −25 ms for IC and less than 75 ms for FO for all terrains. Detection success was over 98%. These results provide preliminary evidence supporting the use of the gyroscope for gait event detection on inclines as well as level walking

On the Interpretation of 3D Gyroscope Measurements

We demonstrate that the common interpretation of angular velocities measured by a 3D gyroscope as being sequential Euler rotations introduces a systematic error in the sensor orientation calculated during motion tracking. For small rotation angles, this systematic error is relatively small and can be mistakenly attributed to different sources of sensor inaccuracies, including output bias drift, inaccurate sensitivities, and alignments of the sensor sensitivity axes as well as measurement noise. However, even for such small angles, due to accumulation over time, the erroneous rotation interpretation can have a significant negative impact on the accuracy of the computed angular orientation. We confirm our findings using real-case measurements in which the described systematic error just worsens the deleterious effects typically attributed to an inaccurate sensor and random measurement noise. We demonstrate that, in general, significant improvement in the angular orientation accuracy can be achieved if the measured angular velocities are correctly interpreted as simultaneous and not as sequential rotations

Design of actuation system and minimization of sensor configuration for gait event detection for Gen 3.0 Portable Powered Ankle-Foot Orthosis (PPAFO)

Powered ankle-foot orthoses (AFOs), which are capable of providing assistive torque at the ankle joint, have significant potential as both assistance and rehabilitation devices. Technology advancements have led to great progression in the development of powered AFOs. Our group had developed the Portable Powered Ankle-Foot Orthosis (PPAFO) that was capable of providing bidirectional assistive torque at the ankle joint. Two generations of the PPAFO were previously developed. Both designs used two different off-the-shelf rotary actuators. This thesis consists of two studies focusing on the development of a new compact higher torque actuation system and the identification of a minimum sensor configuration for gait event detection for a powered AFO. Study 1 presents the design and evaluation of a new actuation system for the PPAFO (Generation 3.0). The actuation system utilized two dual-action linear actuators and a customized gear train. Compared with the previous designs, it generated higher torque and power while providing a thinner lateral profile. The new design had a total weight of (680g) and was capable of generating 32 Nm torque and 110 W power. While running under the same torque and power level as the previous designs, the new design offered better longevity (42.9% and 81.4% increases in normalized run time for test bench emulation and treadmill walking). Although the overall weight of the new actuation system had a 20% increase compared with previous design, it could generate 166.7% more torque and 120% more power, which will enable us to test the system at various torque and power settings. Study 2 investigated the minimum sensor configuration for detecting gait events. Knowledge of the expected orientation and behavior of a limb as related to specific events during the gait cycle (or state timing as a function of the percentage of the gait cycle, % GC) is essential to allow appropriate control of a powered AFO. A total of five sensors were selected (two force sensitive sensors, one ankle angle sensor, and two inertial measurement units (IMU)). The performances of selected sensor configurations were quantified and compared through state-based and event-based approaches in terms of gait state estimation and gait event detection timing, respectively. Gait data were collected from five healthy subjects while walking on a treadmill wearing the Gen 3.0 PPAFO. Results indicated that, while single IMU configurations (located on the shank or foot) both outperformed all other configurations (mean state estimation error: < 2% GC; mean event detection timing error: < 23 ms), the shank IMU was able to detect more gait events than the foot IMU. Since more detectable events could improve the system's robustness (i.e., adjusting to variable speeds) by updating estimation more frequently, a single shank IMU configuration was recommended for powered AFO applications

Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units

This study provides a comparative assessment on the different techniques of classifying human activities performed while wearing inertial and magnetic sensor units on the chest, arms and legs. The gyroscope, accelerometer and the magnetometer in each unit are tri-axial. Naive Bayesian classifier, artificial neural networks (ANNs), dissimilarity-based classifier, three types of decision trees, Gaussian mixture models (GMMs) and support vector machines (SVMs) are considered. A feature set extracted from the raw sensor data using principal component analysis is used for classification. Three different cross-validation techniques are employed to validate the classifiers. A performance comparison of the classifiers is provided in terms of their correct differentiation rates, confusion matrices and computational cost. The highest correct differentiation rates are achieved with ANNs (99.2%), SVMs (99.2%) and a GMM (99.1%). GMMs may be preferable because of their lower computational requirements. Regarding the position of sensor units on the body, those worn on the legs are the most informative. Comparing the different sensor modalities indicates that if only a single sensor type is used, the highest classification rates are achieved with magnetometers, followed by accelerometers and gyroscopes. The study also provides a comparison between two commonly used open source machine learning environments (WEKA and PRTools) in terms of their functionality, manageability, classifier performance and execution times. © 2013 © The British Computer Society 2013. All rights reserved

Accelerometer validity to measure and classify movement in team sports

&nbsp;In team sports accelerometers are used to monitor the physical demands of athletic performance. Daniel\u27s research showed that accelerometer accuracy can be improved through filtering. He also showed that the accelerometer can be used to automatically classify the type of movement performed. Further improving the understanding of team sports

Objective assessment of motor and gait parameters of patients with multiple sclerosis

Multiple Sclerosis (MS) is a chronic inflammatory disease of the central nervous system. It affects approximately 400.000 individuals in Europe and about 2.5 million worldwide. Clinical symptoms of MS are highly variable and depend on the localization of lesions in the brain and spinal cord. Patients with chronic progressive neurological diseases such as MS typically show a decrease of physical activity as compared with healthy individuals. Approximately 75 to 80 percent of patients with MS (PwMS) experience walking and physical activity impairment in early stages of the disease. Therefore, walking impairment is considered as a hallmark symptom as this may have a significant impact on different daily activities. Moreover, an indirect association between overall MS symptoms and physical activity was found. Several studies investigated the walking ability and physical activity under free-living conditions in PwMS, as this may provide significant information to predict the patient’s health status. Different methods have been used for this purpose, including subjective approaches like self-report, questionnaires or diary methods. Although these methods are inexpensive and can easily be employed preferably in large scale studies, they are prone to error due to memory failure and other kind of misreporting. For many years, laboratory analysis systems have been considered to be the “gold standard” for physical activity and walking ability assessment. Nevertheless, these methods require extensive technical support and are unable to assess unconstrained physical activities in free-living situations. Thus, there is increasing interest in ambulatory assessment methods that provide objective measures of physical activity and gait parameters. Therefore, this thesis takes a different approach and investigate the usage of an objective monitoring system to early detect the slightly changes in disease-related walking ability and gait abnormality using one accelerometer. Moreover, this work aims to classify the derived acceleration data regarding their response to a certain intervention and treatment. In doing so, first of all, different algorithms were developed to extract activity and gait parameters in time, frequency and time-frequency domain. Then a Home-based system was developed and provided to help doctors monitor the changes in the ambulatory physical activity of PwMS objectively. The developed system was applied in two different studies over long period of time (one year) to assess changes in physical activity and gait behavior of PwMS and to classify their response to medical treatment. The aim of the first study was to investigate the ability of the developed parameters to objectively capture the changes in motor and walking ability in PwMS. Moreover, the objective was to provide additional evidence from long-term design study that support the association between changes in physical activity and walking ability and disease progression over time. The aim of the second study was to investigate the effectiveness of the medication treatment using the developed gait parameters and the assessment system developed in this work. The result of the study was compared to those assessed in the clinic. Comprehensive analysis of gait features in frequency and time-frequency domain can provide complementary information to understand gait patterns. Therefore, in this study, the parameters peak frequency and energy concentration were integrated along with time-domain parameters, such as step counts and walking speed. In case of chronic diseases, such as MS, medical benefit is the main factor to accept new technology. Thus, the developed system should be advantageous for diagnosis and therapy of MS. Moreover, it is important for the physician to be able to get better overview of the medical data about the disease course and health condition of their patients. Therefore, many critical factors regarding medical, technical and user specific aspects were considered in this work while developing the ambulatory assessment system. To assess the acceptance of the system a questionnaire was designed with main focus on two factors; usefulness and ease-of-use. The questionnaire was based on the Technology Acceptance Model (TAM). As a result, the design, validation and clinical application of Home-based monitoring system and algorithmic methods developed in this thesis offer the opportunity to comprehensively and objectively assess the pattern of behavioral change in physical activity and walking ability using one sensor across prolonged periods of time. The derived information may assist in the process of clinical decision making in the context of neurological rehabilitation and intervention (evaluation of medication or physiotherapy effects) and thus help to eventually improve the patients’ quality of life. In this work the focus was on patients with multiple sclerosis, however the developed and evaluated system can be adapted to other chronic diseases with physical activity disorders and impairment of gait