A System for Monitoring Animals Based on Behavioral Information and Internal State Information.

Managing the risk of injury or illness is an important consideration when keeping pets. This risk can be minimized if pets are monitored on a regular basis, but this can be difficult and time-consuming. However, because only the external behavior of the animal can be observed and the internal condition cannot be assessed, the animal's state can easily be misjudged. Additionally, although some systems use heartbeat measurement to determine a state of tension, or use rest to assess the internal state, because an increase in heart rate can also occur as a result of exercise, it is desirable to use this measurement in combination with behavioral information. In the current study, we proposed a monitoring system for animals using video image analysis. The proposed system first extracts features related to behavioral information and the animal's internal state via mask R-CNN using video images taken from the top of the cage. These features are used to detect typical daily activities and anomalous activities. This method produces an alert when the hamster behaves in an unusual way. In our experiment, the daily behavior of a hamster was measured and analyzed using the proposed system. The results showed that the features of the hamster's behavior were successfully detected. When loud sounds were presented from outside the cage, the system was able to discriminate between the behavioral and internal changes of the hamster. In future research, we plan to improve the accuracy of the measurement of small movements and develop a more accurate system.

Longitudinal assessment of U-shaped and inverted U-shaped developmental changes in the spontaneous movements of infants via markerless video analysis.

Various attempts have been made to elucidate the development patterns in the spontaneous movements of infants through longitudinal evaluations. Movement complexity has been found to demonstrate u-shaped changes in the measurements focusing on limb movements. However, researchers have not yet clarified how other characteristics, besides movement complexity, change over time. This paper presents a longitudinal evaluation of spontaneous movements in infants using evaluation indices calculated through markerless video analysis. Nine infants with corrected ages from [Formula: see text] to 15 weeks participated in the experiments. We confirmed the change in indices over time using statistical methods. Index changes can be classified as positively correlated, u-shaped, inverted u-shaped, and uncorrelated. We also confirmed that the u-shaped and inverted u-shaped indices are negatively correlated. Furthermore, the principal component analysis revealed that the first principal component had the inverted u-shaped changes with the corrected age. These results suggest that it is important to synchronize the inverted u-shaped variations in the movement and velocity with the u-shaped changes in the movement complexity for infant development.

Markerless Measurement and Evaluation of General Movements in Infants.

General movements (GMs), a type of spontaneous movement, have been used for the early diagnosis of infant disorders. In clinical practice, GMs are visually assessed by qualified licensees; however, this presents a difficulty in terms of quantitative evaluation. Various measurement systems for the quantitative evaluation of GMs track target markers attached to infants; however, these markers may disturb infants' spontaneous movements. This paper proposes a markerless movement measurement and evaluation system for GMs in infants. The proposed system calculates 25 indices related to GMs, including the magnitude and rhythm of movements, by video analysis, that is, by calculating background subtractions and frame differences. Movement classification is performed based on the clinical definition of GMs by using an artificial neural network with a stochastic structure. This supports the assessment of GMs and early diagnoses of disabilities in infants. In a series of experiments, the proposed system is applied to movement evaluation and classification in full-term infants and low-birth-weight infants. The experimental results confirm that the average agreement between four GMs classified by the proposed system and those identified by a licensee reaches up to 83.1 ± 1.84%. In addition, the classification accuracy of normal and abnormal movements reaches 90.2 ± 0.94%.

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

Operation assistance for the Bio-Remote environmental control system using a Bayesian Network-based prediction model.

This paper proposes a Bayesian Network (BN) based prediction model for a layer-based selection and its application to an operation assistance for the environmental control system Bio-Remote (BR). In the proposed method, each node of the BN model is involved in the layer-based selection function, which corresponds to an individual operation command, appliance, etc., and previous logs of operation commands and time division are used as input factors to predict the user's intended operation. The prediction results are displayed on the layer-based selection for the BR, and the number of times of operations and time taken for the operations can be reduced with the proposed prediction model. In the experiments, life-logs were collected from a cervical spinal injury patient who used the BR in daily life, and the proposed model was trained based on these recorded life-logs. The prediction accuracy for control devices of the BR system using the proposed model was 84.3 ± 6.5 %. The results indicated that the proposed prediction model could be useful for the operation assistance of the BR system.

A neural network based infant monitoring system to facilitate diagnosis of epileptic seizures.

In this paper, we propose an infant monitoring system that automatically detects epileptic seizures in domestic and hospital environments. The proposed system measures the movements and electroencephalogram (EEG) signals of an infant using a video camera and an electroencephalograph. Seizure features are then extracted from the video images and EEG signals, and the evaluation indices based on medical knowledge are calculated from the features. The system employs a probabilistic neural network for the automatic detection of seizures, thereby allowing the choice/combination of evaluation indices appropriate for the environment via network training. We tested the system in simulated domestic and hospital environments. The validity of the proposed system was reinforced by the results of comparisons with clinical diagnoses.

Bioelectric signal classification using a recurrent probabilistic neural network with time-series discriminant component analysis.

This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.

A quasi-optimal channel selection method for bioelectric signal classification using a partial Kullback-Leibler information measure.

This paper proposes a novel variable selection method involving the use of a newly defined metric called the partial Kullback-Leibler (KL) information measure to evaluate the contribution of each variable (dimension) in the data. In this method, the probability density functions of recorded data are estimated through a multidimensional probabilistic neural network trained on the basis of KL information theory. The partial KL information measure is then defined as the ratio of the values before and after dimension elimination in the data. The effective dimensions for classification can be selected eliminating ineffective ones based on the partial KL information in a one-by-one manner. In the experiments, the proposed method was applied to channel selection with nine subjects (including an amputee), and effective channels were selected from all channels attached to each subject's forearm. The results showed that the number of channels was reduced by 54.3 ±19.1%, and the average classification rate for evaluation data using selected three or four channels was 96.6 ±2.8% (min: 92.1%, max: 100%). These outcomes indicate that the proposed method can be used to select effective channels (optimal or quasi-optimal) for accurate classification.