UNRESTRAINED MEASUREMENT OF ARM MOTION BASED ON A WEARABLE WIRELESS SENSOR NETWORK

Techniques that could precisely monitor human motion are useful in applications such as rehabilitation, virtual reality, sports science, and surveillance. Most of the existing systems require wiring that restrains the natural movement. To overcome this limitation, a wearable wireless sensor network using accelerometers has been developed in this paper to determine the arm motion in the sagittal plane. The system provides unrestrained movements and improves its usability. The lightweight and compact size of the developed sensor node makes its attachment to the limb easy. Experimental results have shown that the system has good accuracy and response rate when compared with a goniometer

NINscope, a versatile miniscope for multi-region circuit investigations

Miniaturized fluorescence microscopes (miniscopes) have been instrumental to monitor neural signals during unrestrained behavior and their open-source versions have made them affordable. Often, the footprint and weight of open-source miniscopes is sacrificed for added functionality. Here, we present NINscope: a light-weight miniscope with a small footprint that integrates a high-sensitivity image sensor, an inertial measurement unit and an LED driver for an external optogenetic probe. We use it to perform the first concurrent cellular resolution recordings from cerebellum and cerebral cortex in unrestrained mice, demonstrate its optogenetic stimulation capabilities to examine cerebello-cerebral or cortico-striatal connectivity, and replicate findings of action encoding in dorsal striatum. In combination with cross-platform acquisition and control software, our miniscope is a versatile addition to the expanding tool chest of open-source miniscopes that will increase access to multi-region circuit investigations during unrestrained behavior

Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist

In this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during normal daily activities as a means to assess their rehabilitation. The method relies on accurately mapping transitions of predefined, standard orientations of the accelerometer to corresponding elementary arm movements. To evaluate the technique, kinematic data was collected from four healthy subjects and four stroke patients as they performed a number of activities involved in a representative activity of daily living, 'making-a-cup-of-tea'. Our experimental results show that the proposed method can independently recognise all three of the elementary upper limb movements investigated with accuracies in the range 91–99% for healthy subjects and 70–85% for stroke patients

Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series

Preparation of NiO catalyst on FeCrAI substrate using various techniques at higher oxidation process

The cheap nickel oxide (NiO) is a potential catalyst candidate to replace the expensive available platinum group metals (PGM). However, the current methods to adhere the NiO powder on the metallic substrates are complicated. Therefore, this work explored the development of nickel oxide using nickel (Ni) on FeCrAl substrate through the combination of nickel electroplating and oxidation process for catalytic converter application. The approach was started with assessment of various nickel electroplating process based on the weight gain during oxidation. Then, the next experiment used the best process in which the pre-treatment using the solution of SiC and/or Al2O3 in methanol. The specimens then were carried out to short term oxidation process using thermo gravimetric analysis (TGA) at 1000 o C. Meanwhile, the long term oxidation process was conducted using an automatic furnace at 900, 1000 and 1100 o C. The atomic force microscopy (AFM) was used for surface analysis in nanometer range scale. Meanwhile, roughness test was used for roughness measurement analysis in micrometer range scale. The scanning electron microscope (SEM) attached with energy dispersive X-ray (EDX) were used for surface and cross section morphology analysis. The specimen of FeCrAl treated using ultrasonic prior to nickel electroplating showed the lowest weight gain during oxidation. The surface area of specimens increased after ultrasonic treatment. The electroplating process improved the high temperature oxidation resistance. In short term oxidation process indicated that the ultrasonic with SiC provided the lower parabolic rate constant (kp) and the Al2O3 and NiO layers were also occurred. The Ni layer was totally disappeared and converted to NiO layer on FeCrAl surface after long term oxidation process. From this work, the ultrasonic treatment prior to nickel electroplating was the best method to adhere NiO on FeCrAl substrate

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

Assessment of walking features from foot inertial sensing

An ambulatory monitoring system is developed for the estimation of spatio-temporal gait parameters. The inertial measurement unit embedded in the system is composed of one biaxial accelerometer and one rate gyroscope, and it reconstructs the sagittal trajectory of a sensed point on the instep of the foot. A gait phase segmentation procedure is devised to determine temporal gait parameters, including stride time and relative stance; the procedure allows to define the time intervals needed for carrying an efficient implementation of the strapdown integration, which allows to estimate stride length, walking speed, and incline. The measurement accuracy of walking speed and inclines assessments is evaluated by experiments carried on adult healthy subjects walking on a motorized treadmill. Root-mean-square errors less than 0.18 km/h (speed) and 1.52% (incline) are obtained for tested speeds and inclines varying in the intervals [3, 6] km/h and [ 5, +15]%, respectively. Based on the results of these experiments, it is concluded that foot inertial sensing is a promising tool for the reliable identification of subsequent gait cycles and the accurate assessment of walking speed and incline

Development of an Ambulatory Device for Monitoring Posture Change and Walking Speed for Use in Rehabilitation

山越, 憲一金沢大学ベンチャー・ビジネス・ラボラトリー金沢大学大学院自然科学研究科知的システム創成Monitoring of posture change in sagittal plane and walking speed is important for evaluate the effectiveness of rehabilitation program or brace. We have developed a wearable device for monitoring human activity. However, in the previous system, there still remain several drawbacks for practical use such as accuracy in angle measurement, cumbersome cable arrangements, and so on. In order to improve these practical drawbacks, a new sensor system was designed, and its availability was evaluated. The results demonstrated that the accuracy of this system showed superior to that of the previous, and this system appears to be a significant means for quantitative assessment of the patient\u27s motion. ©2006 IEEE

Parkinson\u27s Symptoms quantification using wearable sensors

Parkinson’s disease (PD) is a common neurodegenerative disorder affecting more than one million people in the United States and seven million people worldwide. Motor symptoms such as tremor, slowness of movements, rigidity, postural instability, and gait impairment are commonly observed in PD patients. Currently, Parkinsonian symptoms are usually assessed in clinical settings, where a patient has to complete some predefined motor tasks. Then a physician assigns a score based on the United Parkinson’s Disease Rating Scale (UPDRS) after observing the motor task. However, this procedure suffers from inter subject variability. Also, patients tend to show fewer symptoms during clinical visit, which leads to false assumption of the disease severity. The objective of this study is to overcome this limitations by building a system using Inertial Measurement Unit (IMU) that can be used at clinics and in home to collect PD symptoms data and build algorithms that can quantify PD symptoms more effectively. Data was acquired from patients seen at movement disorders Clinic at Sanford Health in Fargo, ND. Subjects wore Physilog IMUs and performed tasks for tremor, bradykinesia and gait according to the protocol approved by Sanford IRB. The data was analyzed using modified algorithm that was initially developed using data from normal subjects emulating PD symptoms. For tremor measurement, the study showed that sensor signals collected from the index finger more accurately predict tremor severity compared to signals from a sensor placed on the wrist. For finger tapping, a task measuring bradykinesia, the algorithm could predict with more than 80% accuracy when a set of features were selected to train the prediction model. Regarding gait, three different analysis were done to find the effective parameters indicative of severity of PD. Gait speed measurement algorithm was first developed using treadmill as a reference. Then, it was shown that the features selected could predict PD gait with 85.5% accuracy