This contribution is concerned with joint angle calculation based on inertial measurement data in the context of human motion analysis. Unlike most robotic devices, the human body lacks even surfaces and right angles. Therefore, we focus on methods that avoid assuming certain orientations in which the sensors are mounted with respect to the body segments. After a review of available methods that may cope with this challenge, we present a set of new methods for: (1) joint axis and position identification; and (2) flexion/extension joint angle measurement. In particular, we propose methods that use only gyroscopes and accelerometers and, therefore, do not rely on a homogeneous magnetic field. We provide results from gait trials of a transfemoral amputee in which we compare the inertial measurement unit (IMU)-based methods to an optical 3D motion capture system. Unlike most authors, we place the optical markers on anatomical landmarks instead of attaching them to the IMUs. Root mean square errors of the knee flexion/extension angles are found to be less than 1° on the prosthesis and about 3° on the human leg. For the plantar/dorsiflexion of the ankle, both deviations are about 1°.

/pdf/imu-based-joint-angle-measurement-for-gait-analysis-2qhn2z6umj.pdf

IMU-based joint angle measurement for gait analysis.

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

/pdf/online-short-term-solar-power-forecasting-1ljgpbshkb.pdf

Online Short-term Solar Power Forecasting

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have limited energy in their primary power storage unit, and this energy may be quickly drained if the sensor node remains operational over long periods of time. Therefore, the idea of harvesting ambient energy from the immediate surroundings of the deployed sensors, to recharge the batteries and to directly power the sensor nodes, has recently been proposed. The deployment of energy harvesting in environmental field systems eliminates the dependency of sensor nodes on battery power, drastically reducing the maintenance costs required to replace batteries. In this article, we review the state-of-the-art in energy-harvesting WSNs for environmental monitoring applications, including Animal Tracking, Air Quality Monitoring, Water Quality Monitoring, and Disaster Monitoring to improve the ecosystem and human life. In addition to presenting the technologies for harvesting energy from ambient sources and the protocols that can take advantage of the harvested energy, we present challenges that must be addressed to further advance energy-harvesting-based WSNs, along with some future work directions to address these challenges.

https://dl.acm.org/doi/pdf/10.1145/3183338

Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review

Non-dispersive infrared (NDIR) gas sensors applied in an environmental field are considered. Disadvantages of the non-dispersive infrared (NDIR) gas sensors include spectral interference and high detection limit. Efforts to improve these disadvantages are reviewed in this paper. Interference caused by water vapor and gas matrix has been partially solved using optical filters and interference correction factors. Limitations such as accuracy and sensitivity of the sensor were overcome by the improvements of inlet gas concentrations, infrared sources, optical designs (including optical filter and gas chamber) and detectors. These improvements are limited to a few gases, in particular, carbon dioxide. Drawbacks related to water vapor still remain and need to be addressed.

A review on non-dispersive infrared gas sensors: Improvement of sensor detection limit and interference correction

This paper presents an analysis of rigid-body joint-angle measurement based on microelectromechanical-system (MEMS) biaxial accelerometers and uniaxial gyroscopes. In comparison to conventional magnetic and optical joint angular sensors, this new inertial sensing principle has the advantages of flexible installation and true contactless sensing. This paper focuses on the comparison of four different inertial-sensor combination methods that are reported in reference papers and utilizes the theory of rigid-body kinematics to explain and analyze their advantages and weaknesses. Experiments have also been conducted to further verify and strengthen the arguments put forward in the analysis. All experiments in this paper took place on a custom-built rigid-body robot arm model that can be manipulated by hand. Sensor calibration and accelerometer alignment issues are also described, and their details are discussed. The experiment results presented in this paper show significant differences with reference to the achieved angular accuracy for various situations when using the four different sensor combination methods. In some cases, the angular error based on one method is more than 0.04 rad, while that from another method is within ±0.005 rad. The noise levels of angular readings from different methods are also experimentally compared and analyzed. The conclusion drawn serves to guide readers toward a suitable method for their particular application.

Joint-Angle Measurement Using Accelerometers and Gyroscopes—A Survey

One-diode photovoltaic model parameters at indoor illumination levels – A comparison

Wireless sensor nodes, as typically realized using IEEE 802.15.4 compatible low-power radio transceivers that offer limited throughput, are generally applicable to low-data rate intermittent monitoring applications. In order to realize high sample rate monitoring applications, it requires either transmitting raw data using a high-throughput radio transceiver or performing computation within the sensor node and then transmitting a small amount of information. In relation to a energy constrained wireless sensing node, a quantitative evaluation of raw data transmission using different short range wireless technologies and in-sensor processing is conducted in this paper. The results, associated with the energy consumption of two data intensive monitoring applications, suggest that in-sensor processing resulting in a small amount of data to be transmitted consumes less energy as compared to that of raw data transmission, even under ideal channel conditions.

A comparative study of in-sensor processing vs. raw data transmission using ZigBee, BLE and Wi-Fi for data intensive monitoring applications

Vibration based condition monitoring is considered to be the most effective method for analyzing the performance of rotating machinery and for early fault detection. Traditional vibration analyzers used for this purpose provide wired interface(s) to connect sensors with the system that analyzes the vibration data. A wireless vibration analyzer can be useful to monitor and analyze the vibration of rotating as well as inaccessible parts of the machinery. However, for a wireless vibration analyzer, both the performance and power consumption are of major concern, especially for real-time tri-axes (horizontal, vertical, and axial) vibration data processing and analyses at a high sampling rate. To evaluate the performance of such an analyzer, we explore different architectures in order to realize a high-performance and low-power wireless vibration analyzer that can be used in addition to traditional analyzers. For this purpose, four different architectures have been implemented in order to evaluate them in terms of performance, power consumption, cost, and design complexity.

Architecture Exploration for a High-Performance and Low-Power Wireless Vibration Analyzer

The characterization of light conditions plays an important role in the estimation of available energy levels to ambient light energy harvesting systems. Indoor light conditions are commonly described by illuminance levels. The same illuminance levels, however, can be generated by different light source types, which radiate different spectral components. This means that based on their spectral response, solar panels can produce different output powers even though identical illuminance levels are observed. We propose a method to distinguish these conditions based on limited spectral information. Using low-cost sensors, spectral characteristics of the light condition can be acquired and used to classify the underlying light source type, which allows for a more accurate estimation of the solar panel response. The method was evaluated experimentally for a number of common indoor light sources and under different conditions. Evaluation results have shown that the method can be used to distinguish the light sources under test with very high classification accuracy. Moreover, the method can be used accurately in situations with limited interference. This makes it a low-cost alternative to the characterization of light conditions using spectrometers, the use of which is infeasible in spatially distributed characterization applications.

Bengt Oelmann

Papers

Joint-Angle Measurement Using Accelerometers and Gyroscopes—A Survey

One-diode photovoltaic model parameters at indoor illumination levels – A comparison

A comparative study of in-sensor processing vs. raw data transmission using ZigBee, BLE and Wi-Fi for data intensive monitoring applications

Architecture Exploration for a High-Performance and Low-Power Wireless Vibration Analyzer

Characterization of Indoor Light Conditions by Light Source Classification