Zhangyan Zhao

Hand Gesture Recognition Research Based on Surface EMG Sensors and 2D-accelerometers

For realizing multi-DOF interfaces in wearable computer system, accelerometers and surface EMG sensors are used synchronously to detect hand movement information for multiple hand gesture recognition. Experiments were designed to collect gesture data with both sensing techniques to compare their performance in the recognition of various wrist and finger gestures. Recognition tests were run using different subsets of information: accelerometer and sEMG data separately and combined sensor data. Experimental results show that the combination of sEMG sensors and accelerometers achieved 5-10% improvement in the recognition accuracies for hand gestures when compared to that obtained using sEMG sensors solely.

Multiple Hand Gesture Recognition Based on Surface EMG Signal

For realizing a multi-DOF myoelectric control system with a minimal number of sensors, research work on the recognition of twenty-four hand gestures based on two-channel surface EMG signal measured from human forearm muscles has been carried out. Third-order AR model coefficients, Mean Absolute Value and Mean Absolute Value ratio of the sEMG signal segments were used as features and the recognition of gestures was performed with a linear Bayesian classifier. Our experimental results show that the proposed two sensors setup and the sEMG signal processing and recognition methods are well suited for distinguishing hand gestures consisting of various wrist motions and single finger extension.

Research on gesture definition and electrode placementin pattern recognition of hand gesture action SEMG

The goal of this study is to explore the effects of electrode placement on the hand gesture pattern recognition performance. We have conducted experiments with surface EMG sensors using two detecting electrode channels. In total 25 different hand gestures and 10 different electrode positions for measuring muscle activities have been evaluated. Based on the experimental results, dependencies between surface EMG signal detection positions and hand gesture recognition performance have been analyzed and summarized as suggestions how to define hand gestures and select suitable electrode positions for a myoelectric control system. This work provides useful insight for the development of a medical rehabilitation system based on EMG technique.

A Prototype of Reflection Pulse Oximeter Designed for Mobile Healthcare

This paper introduces a pulse oximeter prototype designed for mobile healthcare. In this prototype, a reflection pulse oximeter is embedded into the back cover of a smart handheld device to offer the convenient measurement of both heart rate (HR) and SpO2 (estimation of arterial oxygen saturation) for home or mobile applications. Novel and miniaturized circuit modules including a chopper network and a filtering amplifier were designed to overcome the influence of ambient light and interferences that are caused by embedding the sensor into a flat cover. A method based on adaptive trough detection for improved HR and SpO2 estimation is proposed with appropriate simplification for its implementation on mobile devices. A fast and effective photoplethysmogram validation scheme is also proposed. Clinical experiments have been carried out to calibrate and test our oximeter. Our prototype oximeter can achieve comparable performance to a clinical oximeter with no significant difference revealed by paired t -tests (p = 0.182 for SpO2 measurement and p = 0.496 for HR measurement). The design of this pulse oximeter will facilitate fast and convenient measurement of SpO2 for mobile healthcare.

A prototype of gesture-based interface

This paper introduces a novel gesture-based human-machine interface prototype, which consists of a wearable belt embedding with four surface electromyography (SEMG) sensors, a tri-axis accelerometer and an application program running on NOKIA 5800XM. The sensor belt captures hand gestures by acquiring SEMG and acceleration (ACC) signals from forearm, and sends the m out via Bluetooth. The application program receives the data and translates them into control commands of a given interaction application. Experimental results of two test schemes conducted on hand gesture recognition and media player operation demonstrate the validity of the proposed gesture-based interface prototype.

Study on online gesture sEMG recognition

We have realized an online gesture recognition platform for hand gestures using 2-channel surface EMG signals acquired from the forearm. Several features, such as AMV, AMV ratio and fourth-order AR model coefficients are extracted from the sEMG signal and the gesture segments are recognized with a Weighted Euclidean Distance Classifier. An above 90% recognition rate has been achieved with only a 400 µs recognition time. The methods developed in this study are aimed to be applied in a fast-response sEMG control system and be transplanted into an embedded microprocessor system.

An adaptive feature extractor for gesture SEMG recognition

This paper proposes an adaptive feature extraction method for pattern recognition of hand gesture action sEMG to enhance the reusability of myoelectric control. The feature extractor is based on wavelet packet transform and Local Discriminant Basis (LDB) algorithms to select several optimized decomposition subspaces of origin SEMG waveforms caused by hand gesture motions. Then the square roots of mean energy of signal in those subspaces are calculated to form the feature vector. In data acquisition experiments, five healthy subjects implement six kinds of hand motions every day for a week. The recognition results of hand gesture on the basis of the measured SEMG signals from different use sessions demonstrate that the feature extractor is effective. Our work is valuable for the realization of myoelectric control system in rehabilitation and other medical applications.