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

Obstacle Avoidance Method for Electric Wheelchairs Based on a Multi-Layered Non-Contact Impedance Model

This paper proposes an obstacle avoidance method based on a multi-layered non-contact impedance model for control of the biosignal-based electric wheelchair. The proposed system can calculate a virtual repulsive force before the collision by multi-layered impedance fields covered around it. This system therefore regulates desired path to avoid obstacles in a variety of situations. In the experiments, the mobile robot passed through obstacles smoothly, and could stop emergently to avoid the obstacle in front of the robot owing to virtual forces calculated by the proposed model.This work was supported by JSPS KAKENHI Grant Number JP26330226

A Voice Signal-Based Manipulation Method for the Bio-Remote Environment Control System Based on Candidate Word Discriminations

This paper proposes a voice signal-based manipulation method for the Bio-Remote environment control system. The proposed system learns relationships between multiple candidate words’ phonemes extracted by a largevocabulary speaker-independent model and control commands based on a self-learning look-up table. This allows the user to control various devices even if false recognition words are extracted. Experimental results showed that the method accurately discriminate slurred words (average discrimination rate: 93.9±2.40 [%]), and that participants were able to voluntarily control domestic appliances.This work was supported by JSPS KAKENHI Grant Number JP26330226

A Training Method for the Speech Controlled Environmental Control System Based on Candidate Word Discriminations

This paper proposes a concept of a training system for the speech controlled environmental control system: Bio-Remote based on candidate word discriminations. The proposed system can provide three-types of voice signal training: (1) volume, (2) tempo/timing and (3) candidate word which are important for accurate speech recognition based on false recognition results. During the training, such three kinds of features are extracted from measured voice signals and visually and auditory fed back to the user in real time. This allows the user to train speech abilities even if false recognition results are extracted because of slurred speech. The efficacy of the proposed system was demonstrated through training experiments for slurred speech conducted with healthy participants. The results showed that the proposed system was capable for the training of speech abilities.This work was partially supported by JSPS/MEXT KAKENHI Grant Numbers 17K12723 and 26330226

A Human Reaching Movement Model for Myoelectric Prosthesis Control

This paper proposes a reaching movement model for the generation of desired trajectories within a myoelectric prosthesis training system. First, an experiment was performed to observe reaching movements with a non-impaired subject and a myoelectric prosthesis user. Reaching movements made by the prosthesis user were then adopted to construct a model based on a logistic function. The proposed model can be used to generate three trajectory types with a bell-shaped speed profile with the adjustment of only a few parameters.This work was partially supported by a Grant-in-Aid for Young Scientists B Number 26730111

A virtual myoelectric prosthesis training system capable of providing instructions on hand operations

This article proposes a virtual hand and a virtual training system for controlling the MyoBock—the most commonly used myoelectric prosthetic hand worldwide. As the virtual hand is controlled using the method also adopted for the MyoBock hand, the proposed system provides upper-limb amputees with operation sensibilities similar to those experienced in MyoBock control. It can also display an additional virtual hand for the provision of instructions on hand operation, such as the recommended posture for object grasping and the trajectory desirable to reach a target. In virtual hand control experiments conducted with an amputee to evaluate the proposed virtual hand’s operability, the subject successfully performed stable opening and closing with high discrimination rates (89.3±6.65%), thanks to the virtual hand’s incorporation of the MyoBock’s operational characteristics. A training experiment using the proposed system was also conducted with eight healthy participants over a period of 5 days. The participants were asked to perform the box and block test using the MyoBock hand in a real environment on the first and final days. The results showed that the number of blocks transported in 1 min significantly increased and that the participants using the instruction virtual hand changed the orientation of the hand approaching blocks from vertical to lateral. The outcomes of the experiment indicate that the proposed system can be used to improve MyoBock hand control operation both quantitatively and qualitatively.This work was partially supported by a Grant-in-Aid for Young Scientists B Number 26730111 and a Grant-in-Aid for Scientific Research C Number 26462242

Speech controlled environmental control system: Bio-Remote for the physically challenged based on candidate word discriminations

研究成果の概要（和文）：本研究では，誤認識に基づく音声操作型環境制御装置：Bio-Remoteを開発するとともに，行動の予測モデルに基づく操作支援を実現した．そして，肢体不自由者の自立生活を支援するためにBio-Remoteシステムを応用したロボットマニピュレータ制御および衝突回避機能を有する移動型ロボットについて検討を行った．研究成果の概要（英文）：This study proposed a speech controlled environmental control system (ECS): Bio-Remote (BR) based on candidate word discriminations and realized operation assistance using a Bayesian network-based prediction model. In order to maintain an independent life for the physically challenged, this study also proposed robotic manipulator control based on the BR, and obstacle avoidance method for mobile robots