Hyonyoung Han

Artifacts in wearable photoplethysmographs during daily life motions and their reduction with least mean square based active noise cancellation method

Signal distortion of photoplethysmographs (PPGs) due to motion artifacts has been a limitation for developing real-time, wearable health monitoring devices. The artifacts in PPG signals are analyzed by comparing the frequency of the PPG with a reference pulse and daily life motions, including typing, writing, tapping, gesturing, walking, and running. Periodical motions in the range of pulse frequency, such as walking and running, cause motion artifacts. To reduce these artifacts in real-time devices, a least mean square based active noise cancellation method is applied to the accelerometer data. Experiments show that the proposed method recovers pulse from PPGs efficiently.

Development of a wearable health monitoring device with motion artifact reduced algorithm (ICCAS 2007)

In this paper, a real-time, wearable and motion artifact reduced health monitoring device is represented. A finger band, wearable health monitoring device, is consists of photoplethysmography (PPG) sensor, 3-axis accelerometer, microprocessor and wireless module. The PPG sensor acquire distorted heart beat signal due to motion artifact. The finger movements are detected using the accelerometer, and major motion directions causing of the noise are researched by comparing each directional motion signals and distorted PPG signal. Two directional motions are significantly related to noise, therefore, these two directional active noise cancellation algorithm was applied to reconstruct the noise added heart beat signal. Low order (4th order) NLMS (normalized least mean square) adaptive filter is employed for small size wearable device. The finger band device is experimented in daily body motion condition (1-3 Hz), and reduce distortion rate less than 5% by active noise cancellation algorithm. The motion artifact reduced finger band sensor can offer continuous health monitoring without daily motion artifact.

Optical muscle activation sensors for estimating upper limb force level

Muscle activation sensors are playing an important role in motion intention sensing of human-machine interaction system, such as human assisting manipulators or prosthetic devices. There are requirements of low-cost, reliability and portability for the device. This paper proposes an optical muscle activation sensor (oMAS) which measures the optical density in muscle by emitting and gathering the single wavelength light source. The oMAS was tested on the biceps brachii in isometric contraction condition, comparison with muscle activation level from surface electromyography. The results show that the optical density is enough to discriminable the muscle activation level. Therefore the oMAS is usable to measure the muscle contraction and it could offer a low-cost, reliable, portable and comfortable alternative for current muscle activation sensors.

Novel muscle activation sensors for estimating of upper limb motion intention

Measurement of muscle activation is important to understand body motion and the exertion of force. This paper presents two novel muscle activation sensors, a piezo cable muscle activation sensor (pMAS) and an optical muscle activation sensor (oMAS). The pMAS measures variations of a flexible piezo cable band that originate from diameter changes of muscle bundles. The sensors are easily attached and can be worn on clothes. The oMAS, which measures the optical density of muscle fibers, has advantages of small size, ease of use, and non-referenced individual sensing. Muscle activations of the upper limb during movements were collected to evaluate the performance of the proposed pMAS, and oMAS, respectively. Furthermore, the relation between movements and sensor signals was analyzed to estimate the upper limb movements.

Prediction of the Upper Limb Motion Based on a Geometrical Muscle Changes for Physical Human Machine Interaction

Estimation methods of motion intention from bio-signal present challenges in man machine interaction(MMI) to offer user’s command to machine without control of any devices. Measurements of meaningful bio-signals that contain the motion intention and motion estimation methods from bio-signal are important issues for accurate and safe interaction. This paper proposes a novel motion estimation sensor based on a geometrical muscle changes, and a motion estimation method using the sensor. For estimation of the motion, we measure the circumference change of the muscle which is proportional to muscle activation level using a flexible piezoelectric cable (pMAS, piezo muscle activation sensor), designed in band type. The pMAS measures variations of the cable band that originate from circumference changes of muscle bundles. Moreover, we estimate the elbow motion by applying the sensor to upper limb with least square method. The proposed sensor and prediction method are simple to use so that they can be used to motion prediction device and methods in rehabilitation and sports fields.

Influence of motion artifacts on photoplethysmographic signals for measuring pulse rates

One of the most important issues in the wearable healthcare sensors for continuous monitoring in daily life is motion artifact reduction. This paper presents an analysis of motion artifacts on PPG signals to find the suitable location for monitoring heart rates. The experiment system, which consists of PPG sensors and data acquisition system, was built to measure heart rate during controlled body motions such as walking. A reflective type PPG sensor was used to measure the pulse rate signal from several locations of the body including forehead, ear, neck, wrist, finger, and toe, and data were collected and analyzed to compare the effects of motion artifacts on signals. Pulse rates were estimated by counting the number of heartbeats in minute and comparing results with from the two states of stationary and moving conditions. The experiment results showed that forehead is the most suitable location for monitoring because it has less motion artifact than other locations. These results can be used to support the measurement of pulse rates to detect clinically significant heartbeat problems.

Development of real-time muscle stiffness sensor based on resonance frequency for physical Human Robot Interactions

This paper presents a new type of muscle contraction sensor for motion intention detection algorithm in physical human robot interaction (pHRI). The resonance frequency shift by muscle contraction was measured by piezoelectric material. The developed sensor can measure muscle activations accurately over clothes and this is an advantage over the conventional surface Electromyography (sEMG). Performances of the sensor are evaluated through isometric wrist flexion motion tests based on maximal voluntary contraction (MVC) in two aspects: accuracy and speed. While the flexor carpi radialis (FCR) contraction tests up to 40% MVC, sensor outputs are compared with force sensor outputs. The result shows that we can measure muscle contraction by the developed sensor with high correlation and fast response, which is desirable for many physical human robot interactions including exoskeleton devices.

sEMG pattern classification using hierarchical Bayesian model

This work addresses surface electromyogram (sEMG)-based muscle pattern classification using a generative model. By using a hierarchical Bayesian model, the proposed approach constructs an overall process model of recorded sEMG signals. By inferring probabilistically latent neural states which governs a collection of training sEMG data, classification is realized. To validate the approach, eight-class classification using four sEMG sensors on the limb actions is tested with five subjects. The proposed model achieves an overall 95% accuracy in the classification experiment. The results support that the proposed approach is very promising for sEMG pattern classification.