An adaptive orientation misalignment calibration method for shoulder movements using inertial sensors : a feasibility study

Qualitative assessment of the progress in physical rehabilitation largely depends on accurate measurement of the range of movements and other kinematic parameters. In clinical practice, wearable inertial sensors have proved to be a potential candidate for such measurements, over the traditional marker based optical systems due to cost and space considerations. The accuracy of wearable sensors have a significant dependence on the initial orientation calibration and the assumption that the sensor will not slip or move with respect to the attached limb. This article introduces a novel calibration algorithm to correct initial orientation misalignment, as well as to track and correct subsequent alignment errors progressively throughout the experiment. The theoretical assertions are validated through controlled experiments with simulated accelerometer and gyroscope measurements.

An adaptive complementary filter for inertial sensor based data fusion to track upper body motion

  Remote human activity monitoring is critical and essential in physiotherapy with respect to the skyrocketing healthcare expenditure and the fast aging population. One of frequently used method to monitor human activity is wearing inertial sensors since it is low-cost and accurate. However, the measurements of those sensors are able only to estimate the orientation and rotation angles with respect to actual movement angles, because of differences in the body’s co-ordination system and the sensor’s co-ordination system. There were numerous studies being conducted to improve the accuracy of estimation, though there is potential for further discussions on improving accuracy by replacing heavy algorithms to less complexity. This research is an attempt to propose an adaptive complementary filter for identifying human upper arm movements. Further, this article discusses a feasibility of upper arm rehabilitation using the proposed adaptive complementary filter and inertial measurement sensors. The proposed algorithm is tested with four healthy subjects wearing an inertial sensor against gold standard, which is the VICON system. It demonstrated root mean squared error of 8.77◦ for upper body limb orientation estimation when compared to gold standard VICON optical motion capture system.

Ambulatory energy expenditure evaluation for treadmill exercises

This paper introduces an ambulatory energy expenditure technique using a single inertial sensor, and compares the performance with an industry standard metabolic measurement system. Wearable energy expenditure estimation systems are key instruments in athlete evaluation. The cost and size of traditional oxygen intake measurement systems (VO2 systems) limits usage of such technology in everyday athlete training and evaluation events. This project describes a method of estimating energy expenditure during treadmill exercise, from limb angular velocity and metabolic measurements. The feasibility of using such a system was evaluated using experimental results.

The study to track human arm kinematics applying solutions of Wahba’s Problem upon inertial/magnetic sensors

Long-term, off-site human monitoring systems are emerging with respect to the skyrocketing expenditures engaged with rehabilitation therapies for neurological diseases. Inertial/magnetic sensor modules are well known as a worthy solution for this problem. Much attention and effort are being paid for minimizing drift problem of angular rates, yet the rest of kinematic measurements (earth’s magnetic field and gravitational orientation) are only themselves capable enough to track movements applying the theory for solving historicalWahbas Problem. Further, these solutions give a closed form solution which makes it mostly suitable for real time Mo-Cap systems. This paper examines the feasibility of some typical solutions of Wahba’s Problem named TRIAD method, Davenport’s q method, Singular Value Decomposition method and QUEST algorithm upon current inertial/magnetic sensor measurements for tracking human arm movements. Further, the theoretical assertions are compared through controlled experiments with both simulated and actual accelerometer and magnetometer measurements.

Controlling the relative orientation between the two magnetic fields of a synchronous motor

A simple and reliable method for controlling the relative orientation between the two magnetic fields of a permanent magnet synchronous motor is presented. Finding the initial (at motor powering-up time) value of this relative location is essential for the proper operation of the motor. The feedback control loop used finds this initial relative orientation quickly. Further, using the proposed method allows considerable cost saving, as a transducer that is usually used for this purpose can be eliminated. The cost saving is most obvious in the case of linear motors and angle motors with large diameters. The way the problem is posed is an essential part of this work and it is the reason behind the apparent simplicity of the solution. The method relied upon a single sensor, and it has been tested when a relative encoder was used

Wireless sensor networks for in-situ image validation for water and nutrient management

Water and Nitrogen (N) are critical inputs for crop production. Remote sensing data collected from multiple scales, including ground-based, aerial, and satellite, can be used for the formulation of an efficient and cost effective algorithm for the detection of N and water stress. Formulation and validation of such techniques require continuous acquisition of ground based spectral data over the canopy enabling field measurements to coincide exactly with aerial and satellite observations. In this context, a wireless sensor in situ network was developed and this paper describes the results of the first phase of the experiment along with the details of sensor development and instrumentation set up. The sensor network was established based on different spatial sampling strategies and each sensor collected spectral data in seven narrow wavebands (470, 550, 670, 700, 720, 750, 790 nm) critical for monitoring crop growth. Spectral measurements recorded at required intervals (up to 30 seconds) were relayed through a multi-hop wireless network to a base computer at the field site. These data were then accessed by the remote sensing centre computing system through broad band internet. Comparison of the data from the WSN and an industry standard ground based hyperspectral radiometer indicated that there were no significant differences in the spectral measurements for all the wavebands except for 790nm. Combining sensor and wireless technologies provides a robust means of aerial and satellite data calibration and an enhanced understanding of issues of variations in the scale for the effective water and nutrient management in wheat.

Calibration of audio-video sensors for multi-modal event indexing

This paper addresses the coordinated use of video and audio cues to capture and index surveillance events with multimodal labels. The focus of this paper is the development of a joint-sensor calibration technique that uses audio-visual observations to improve the calibration process. One significant feature of this approach is the ability to continuously check and update the calibration status of the sensor suite, making it resilient to independent drift in the individual sensors. We present scenarios in which this system is used to enhance surveillance.

A novel non-enzymatic glucose sensor based on nickel (II) oxide electrospun nanofibers

A new enzymeless glucose sensor has been fabricated via electrospinning technology and subsequent calcination. The morphology and structure of the as-prepared nanofibers have been characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). The electrocatalytic oxidation of glucose in alkaline medium at nickel oxide modified glassy carbon electrodes has been investigated. The modified electrodes offer excellent electrocatalytic activity toward the glucose oxidation at low positive potential (0.3 V). Glucose has been determined chronoamperometrically at the surface of NiO nanofibers modified electrode in 0.5 mM NaOH. Under the optimized condition, the calibration curve is linear in the concentration range of 2 × 10−3 mM∼1 mM, and 1 mM∼9.5 mM. The detection limit (signal-to-noise 3) and response time are 3.394 × 10−6 M and 2 s, respectively. The NiO electrospun nanofibers is easy to prepare and feasible in economy. The modified electrode is steady and can be used repeatedly, so it is reasonable to expect its broad use in non-enzymatic glucose sensor.

Remote monitoring system enabling cloud technology upon smart phones and inertial sensors for human kinematics

Stroke is a common neurological condition which is becoming increasingly common as the population ages. This entails healthcare monitoring systems suitable for home use, with remote access for medical professionals and emergency responders. The mobile phone is becoming the easy access tool for self-evaluation of health, but it is hindered by inherent problems including computational power and storage capacity. This research proposes a novel cloud based architecture of a biomedical system for a wearable motion kinematic analysis system which mitigates the above mentioned deficiencies of mobile devices. The system contains three subsystems: 1. Bio Kin WMS for measuring the acceleration and rotation of movement 2. Bio Kin Mobi for Mobile phone based data gathering and visualization 3. Bio Kin Cloud for data intensive computations and storage. The system is implemented as a web system and an android based mobile application. The web system communicates with the mobile application using an encrypted data structure containing sensor data and identifiable headings. The raw data, according to identifiable headings, is stored in the Amazon Relational Database Service which is automatically backed up daily. The system was deployed and tested in Amazon Web Services.

Separation of longitudinal and flexural wave in a cylindrical structure based on sensor arrangement for non-destructive evaluation

Low strain integrity testing is commonly used to assess the in situ condition of the poles or piles. For poles, it is important to calculate the embedment length and location of damage which is highly influenced by the accurate determination of the wave velocity. In general, depending on impact location and orientation, both longitudinal and bending waves may generate inside the pole, and these two waves have very distinct characteristics and wave velocity. These differences are even more prominent in the low frequency which is usually induced in the low strain non-destructive testing. Consequently, it will be useful if these two waves can be separated for the condition assessment of the poles. In this paper, a numerical analysis is performed on a pole considering that both waves are generated, and a method is proposed to differentiate these two waves based on an appropriate sensor arrangement that includes the location and the orientation of the sensors. Continuous wavelet transform is applied on the numerical signal to calculate the phase velocity of the waves and compared with analytical phase velocity curves. From the results, it can be seen that appropriate location and orientation of the sensors can separate the longitudinal and flexural waves as they match significantly well with the corresponding analytical phase velocity curves of these two waves.

Geospatial estimation-based auto drift correction in wireless sensor networks

Wireless sensor networks are often deployed in large numbers, over a large geographical region, in order to monitor the phenomena of interest. Sensors used in the sensor networks often suffer from random or systematic errors such as drift and bias. Even if they are calibrated at the time of deployment, they tend to drift as time progresses. Consequently, the progressive manual calibration of such a large-scale sensor network becomes impossible in practice. In this article, we address this challenge by proposing a collaborative framework to automatically detect and correct the drift in order to keep the data collected from these networks reliable. We propose a novel scheme that uses geospatial estimation-based interpolation techniques on measurements from neighboring sensors to collaboratively predict the value of phenomenon being observed. The predicted values are then used iteratively to correct the sensor drift by means of a Kalman filter. Our scheme can be implemented in a centralized as well as distributed manner to detect and correct the drift generated in the sensors. For centralized implementation of our scheme, we compare several krigingand nonkriging-based geospatial estimation techniques in combination with the Kalman filter, and show the superiority of the kriging-based methods in detecting and correcting the drift. To demonstrate the applicability of our distributed approach on a real world application scenario, we implement our algorithm on a network consisting of Wireless Sensor Network (WSN) hardware. We further evaluate single as well as multiple drifting sensor scenarios to show the effectiveness of our algorithm for detecting and correcting drift. Further, we address the issue of high power usage for data transmission among neighboring nodes leading to low network lifetime for the distributed approach by proposing two power saving schemes. Moreover, we compare our algorithm with a blind calibration scheme in the literature and demonstrate its superiority in detecting both linear and nonlinear drifts.

Human bio-kinematic parameter estimation using inertial sensors.

This thesis focuses on accurately capturing bio-kinematic parameters for physical tele-rehabilitation using measurements from inertial sensors. Contributions are: accurately capturing human kinematics despite intrinsic uncertainties omnipresent with human movements, improving the tracking accuracy by correcting the sensor misalignment and assessing rehabilitation exercises for evaluating the progress of people with disabilities.