Sensor-based Algorithms Research Articles

A database of physical therapy exercises with variability of execution collected by wearable sensors

This document introduces the PHYTMO database, which contains data from physical therapies recorded with inertial sensors, including information from an optical reference system. PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. PHYTMO includes magneto-inertial data, together with a highly accurate location and orientation in the 3D space provided by the optical system. The files were stored in CSV format to ensure its usability. The aim of this dataset is the availability of data for two main purposes: the analysis of techniques for the identification and evaluation of exercises using inertial sensors and the validation of inertial sensor-based algorithms for human motion monitoring. Furthermore, the database stores enough data to apply Machine Learning-based algorithms. The participants’ age range is large enough to establish age-based metrics for the exercises evaluation or the study of differences in motions between different groups.

Novel ways to use sensor data to improve mastitis management

Current sensor systems are used to detect cows with clinical mastitis. Although, the systems perform well enough to not negatively affect the adoption of automatic milking systems, the performance is far from perfect. An important advantage of sensor systems is the availability of multiple measurements per day. By clearly defining the need for detection of subclinical mastitis (SCM) and clinical mastitis (CM) from the farmers' management perspective, detection and management of SCM and CM may be improved. Sensor systems may also be used for other aspects of mastitis management. In this paper we have defined 4 mastitis situations that could be managed with the support of sensor systems. Because of differences in the associated management and the epidemiology of these specific mastitis situations, the required demands for performance of the sensor systems do differ. The 4 defined mastitis situations with the requirements of performance are the following: (1) Cows with severe CM needing immediate attention. Sensor systems should have a very high sensitivity (>95% and preferably close to 100%) and specificity (>99%) within a narrow time window (maximum 12 h) to ensure that close to all cows with true cases of severe CM are detected quickly. Although never studied, it is expected that because of the effects of severe CM, such a high detection performance is feasible. (2) Cows with mastitis that do not need immediate attention. Although these cows have a risk of progressing into severe CM or chronic mastitis, they should get the chance to cure spontaneously under close monitoring. Sensor alerts should have a reasonable sensitivity (>80%) and a high specificity (>99.5%). The time window may be around 7 d. (3) Cows needing attention at drying off. For selective dry cow treatment, the absence or presence of an intramammary infection at dry-off needs to be known. To avoid both false-positive and false-negative alerts, sensitivity and specificity can be equally high (>95%). (4) Herd-level udder health. By combining sensor readings from all cows in the herd, novel herd-level key performance indicators can be developed to monitor udder health status and development over time and raise alerts at significant deviances from predefined thresholds; sensitivity should be reasonably high, >80%, and because of the costs for further analysis of false-positive alerts, the specificity should be >99%. The development and validation of sensor-based algorithms specifically for these 4 mastitis situations will encourage situation-specific farmer interventions and operational udder health management.

Real-Time Detection of Actual and Early Gait Events During Level-Ground and Ramp Walking

In recent studies on gait event detection, the inertial sensor-based approaches were found to be more suitable for long term use compared to force sensors. However, inertial sensor-based algorithms exhibit some time delay. This time delay can affect the stability of the powered prosthetic and assistive devices (PPADs). Hence, several methods were proposed to reduce the same. However, it is impossible to completely eliminate the effect of delay using the existing methods. In this study, we proposed a novel early detection strategy to tackle the issue discussed above. The strategy involved the detection of both actual as well as early gait events. Thus, a total of nine gait events were detected in real-time using inertial sensors and a set of rules. The proposed method was tested for ten control subjects during both level ground as well as ramp walking, and the detection accuracy was found to be equal to 100%. Moreover, the time delay for the detection of actual gait events using the proposed method was found to be similar to that of some state-of-the-art methods and better than a few. The average early detection time was found to be equal to 123 ms that can be utilized to improve the response of PPADs.

Design and Control of Soft Unmanned Aerial Vehicle ‘S-CLOUD’

Short time-of-flight and safety concerns owing to low efficiency and fast rotation of propellers, respectively, are hurdles in the usage of drones. Usually, drones are carrying out missions that distant from the people like aerial photography, measurement, delivery, and surveillance, and, thus, it is not allowed to deploy drones above crowds. In this article, we present a novel soft drone called S-CLOUD that has longer time-of-flight (more than an hour) and higher user safety. It is composed of a torus-shaped envelope made of soft material, filled with Helium gas, and a center flow control unit mounted at the center of the envelope for producing translational ( $X$ -, $Y$ -, and $Z$ -axes) and rotational ( $Z$ -axis) motions. In this article, the control unit and envelope design are described in detail, and the characteristics of the drone are explained. Moreover, sensor-based feedback control algorithms and their experimental results are described. Finally, applications with S-CLOUD exhibiting its feasibility are also presented.

Validation of Novel Relative Orientation and Inertial Sensor-to-Segment Alignment Algorithms for Estimating 3D Hip Joint Angles.

Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the attention in this area, algorithms for estimating hip joint angles are less available. Herein, we propose and validate a novel algorithm for this purpose with innovations in sensor-to-sensor orientation and sensor-to-segment alignment. The proposed approach is robust to sensor placement and does not require specific calibration motions. The accuracy of the proposed approach is established relative to optical motion capture and compared to existing methods for estimating relative orientation, hip joint angles, and range of motion (ROM) during a task designed to exercise the full hip range of motion (ROM) and fast walking using root mean square error (RMSE) and regression analysis. The RMSE of the proposed approach was less than that for existing methods when estimating sensor orientation ( and vs. and ) and flexion/extension joint angles ( and vs. and ). Also, ROM estimation error was less than during the walking trial using the proposed method. These results suggest the proposed approach presents an improvement to existing methods and provides a promising technique for remote monitoring of hip joint angles.

6축 관성센서를 이용한 센서가속도 추정용 칼만필터

Although an accelerometer is a sensor that measures acceleration, it cannot be used by itself to measure the acceleration when the orientation of the sensor changes. This paper introduces a Kalman filter for the estimation of a sensor acceleration based on a six-axis inertial sensor (i.e., a three-axis accelerometer and three-axis gyroscope). The novelty of the proposed Kalman filter lies in the fact that its state vector includes not only the tilt angle variable but also the sensor acceleration. Thus, the filter can explicitly estimate the latter with a high accuracy. The accuracy of acceleration estimates were validated experimentally under three different dynamic conditions, using an optical motion capture system. It could be concluded that the performance of the proposed Kalman filter was comparable to that of the state-of-the-art estimation algorithm employed by the Xsens MTw. The proposed algorithm may be more suitable than inertial/magnetic sensor-based algorithms for various applications adopting six-axis inertial sensors.

On the Sensor-Based Navigation Under Position and Orientation Errors

A mobile robot usually estimates its position and orientation by a dead reckoning system. However under several kinds of uncertainties, e.g. inner/outer sensor errors and/or a slip, a mobile robot unfortunately misunderstands true position and orientation in a real world as false position and orientation in a computer world. Due to the difference, a mobile robot governed by sensor-based navigation sometimes gets lost in an unknown 2-D environment. On the observation, in this paper, we firstly improve previous metric sensor-based navigation algorithms in order for a mobile robot to deal with such uncertainties. Secondly, we name a class of errors a homeomorpic error, where true and false positions in real and computer worlds are projected each other by the homeomorphism. Finally, we theoretically show that generation of the homeomorpic error is the necessary and sufficient condition for a mobile robot not to enter into any limit cycle in every type of sensor-based navigation algorithm.

障害物の幾何学的性質に基づくセンサベーストデッドロックフリーナビゲーションアルゴリズム

障害物の幾何学的性質に基づくセンサベーストデッドロックフリーナビゲーションアルゴリズム

On a Sensor-Based Navigation for a Mobile Robot

The sensor-based navigation for a mobile robot is a problem of how to select a sequence of sensor-based behaviors between start and goal positions. If a mobile robot does not know its 2-d environment completely or partially, it is obliged to rely on sensor information reflected from closer obstacles in order to avoid them on-line. In the on-line framework, we should consider how a mobile robot reaches its goal position in an uncertain 2-d world. Therefore we will study some previous sensor-based navigation algorithms for mobile robots. Our motivations are to ascertain the convergence of a mobile robot to its goal position, compare the lengths of sensor-based sequences made in the previous algorithms, and decrease the length of a sequence of sensor-based motions, which is generated between start and goal positions by a sensor-feedback obstacle avoidance. Because the mobile robot itself, its sensors, and its environment usually have several uncertainties, it is notable as to how a mobile robot arrives at or near its goal in overcoming such uncertainties. It is demonstrated that the sensor-based navigation still has an enormous potential as an actual navigation of a mobile robot in a completely or partially unknown environment.

センサベーストパスプランニングをデッドロックフリーにする十分条件の関数的一般化について

センサベーストパスプランニングをデッドロックフリーにする十分条件の関数的一般化について

Multirate state and parameter estimation in an antibiotic fermentation with delayed measurements

This article discusses issues related to estimation and monitoring of fermentation processes that exhibit endogenous metabolism and time-varying maintenance activity. Such culture-related activities hamper the use of traditional, software sensor-based algorithms, such as the extended kalman filter (EKF). In the approach presented here, the individual effects of the endogenous decay and the true maintenance processes have been lumped to represent a modified maintenance coefficient, m(c). Model equations that relate measurable process outputs, such as the carbon dioxide evolution rate (CER) and biomass, to the observable process parameters (such as net specific growth rate and the modified maintenance coefficient) are proposed. These model equations are used in an estimator that can formally accommodate delayed, infrequent measurements of the culture states (such as the biomass) as well as frequent, culture-related secondary measurements (such as the CER). The resulting multirate software sensor-based estimation strategy is used to monitor biomass profiles as well as profiles of critical fermentation parameters, such as the specific growth for a fed-batch fermentation of Streptomyces clavuligerus.

A Real-time Algorithm for Obstacle Avoidance of Autonomous Mobile Robots

SUMMARYA real-time obstacle avoidance algorithm is proposed for autonomous mobile robots. The algorithm is sensor-based and consists of a H-mode and T-mode. The algorithm can deal with a complicated obstacle environment, such as multiple concave and convex obstacles. It will be shown that the algorithm is more efficient and more robust than other sensor-based algorithms. In addition, the algorithm will guarantee a solution for the obstacle avoidance problem. Since the algorithm only takes up a small computational time, it can be implemented in real time.