The absence of the queen in a beehive is a very strong indicator of the need for beekeeper intervention. Manually searching for the queen is an arduous recurrent task for beekeepers that disrupts the normal life cycle of the beehive and can be a source of stress for bees. Sound is an indicator for signalling different states of the beehive, including the absence of the queen bee. In this work, we apply machine learning methods to automatically recognise different states in a beehive using audio as input. We investigate both support vector machines and convolutional neural networks for beehive state recognition, using audio data of beehives collected from the NU-Hive project. Results indicate the potential of machine learning methods as well as the challenges of generalizing the system to new hives.

BackgroundThe human activity monitoring technology is one of the most important technologies for ambient assisted living, surveillance-based security, sport and fitness activities, healthcare of elderly people. The activity monitoring is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a low-cost wearable wireless system specifically designed to acquire surface electromyography (sEMG) and accelerometer signals for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications.ResultsThe proposed system consists of several ultralight wireless sensing nodes that are able to acquire, process and efficiently transmit the motion-related (biological and accelerometer) body signals to one or more base stations through a 2.4 GHz radio link using an ad-hoc communication protocol designed on top of the IEEE 802.15.4 physical layer. A user interface software for viewing, recording, and analysing the data was implemented on a control personal computer that is connected through a USB link to the base stations. To demonstrate the capability of the system of detecting the user’s activity, data recorded from a few subjects were used to train and test an automatic classifier for recognizing the type of exercise being performed. The system was tested on four different exercises performed by three people, the automatic classifier achieved an overall accuracy of 85.7% combining the features extracted from acceleration and sEMG signals.ConclusionsA low cost wireless system for the acquisition of sEMG and accelerometer signals has been presented for healthcare and fitness applications. The system consists of wearable sensing nodes that wirelessly transmit the biological and accelerometer signals to one or more base stations. The signals so acquired will be combined and processed in order to detect, monitor and recognize human activities.

This paper proposes an improvement in cross-correlation methods derived from the Lee-Schetzen method, in order to obtain a lower mean square error in the output for a wider range of the input variances. In particular, each Wiener kernel is identified with a different input variance and new formulas for conversion from Wiener to Volterra representation are presented.

The widespread decline of honey bee (Apis mellifera L.) colonies registered in recent years has raised great attention to the need of gathering deeper knowledge about this phenomenon, by observing the colonies’ activity to identify possible causes, and design corresponding countermeasures. In fact, honey bees have well-known positive effects on both the environment and human life, and their preservation becomes critical not only for ecological reasons, but also for the social and economic development of rural communities. Smart sensor systems are being developed for real-time and long-term measurement of relevant parameters related to beehive conditions, such as the hive weight, sounds emitted by the bees, temperature, humidity, and CO 2 inside the beehive, as well as weather conditions outside. This paper presents a multisensor platform designed to measure the aforementioned parameters from beehives deployed in the field, and shows how the fusion of different sensor measurements may provide insights on the status of the colony, its interaction with the surrounding environment, and the influence of climatic conditions.

A periodic sequence is defined as a perfect periodic sequence for a certain nonlinear filter if the cross-correlation between any two of the filter basis functions, estimated over a period, is zero. Using a perfect periodic sequence as input signal, an unknown nonlinear system can be efficiently identified with the cross-correlation method. Moreover, the basis functions that guarantee the most compact representation according to some information criterion can also be easily estimated. Perfect periodic sequences have already been developed for even mirror Fourier, Legendre and Chebyshev nonlinear filters. In this paper, we show they can be developed also for nonlinear Wiener filters. Their development is non-trivial and differs from that of the other nonlinear filters, since Wiener filters have orthogonal basis functions for white Gaussian input signals. Experimental results highlight the usefulness of the proposed perfect periodic sequences in comparison with the Gaussian input signals commonly used for Wiener filter identification

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This paper presents a technique for parametric model estimation of the motor unit action potential (MUAP) from the surface electromyography (sEMG) signal by using homomorphic deconvolution. The cepstrum-based deconvolution removes the effect of the stochastic impulse train, which originates the sEMG signal, from the power spectrum of sEMG signal itself. In this way, only information on MUAP shape and amplitude were maintained, and then, used to estimate the parameters of a time-domain model of the MUAP itself. In order to validate the effectiveness of this technique, sEMG signals recorded during several biceps curl exercises have been used for MUAP amplitude and time scale estimation. The parameters so extracted as functions of time were used to evaluate muscle fatigue showing a good agreement with previously published results.