Shih-Chin Yang

A Wearable Inertial Measurement System With Complementary Filter for Gait Analysis of Patients With Stroke or Parkinson’s Disease

This paper presents a wearable inertial measurement system and its associated spatiotemporal gait analysis algorithm to obtain quantitative measurements and explore clinical indicators from the spatiotemporal gait patterns for patients with stroke or Parkinson’s disease. The wearable system is composed of a microcontroller, a triaxial accelerometer, a triaxial gyroscope, and an RF wireless transmission module. The spatiotemporal gait analysis algorithm, consisting of procedures of inertial signal acquisition, signal preprocessing, gait phase detection, and ankle range of motion estimation, has been developed for extracting gait features from accelerations and angular velocities. In order to estimate accurate ankle range of motion, we have integrated accelerations and angular velocities into a complementary filter for reducing the accumulation of integration error of inertial signals. All 24 participants mounted the system on their foot to walk along a straight line of 10 m at normal speed and their walking recordings were collected to validate the effectiveness of the proposed system and algorithm. Experimental results show that the proposed inertial measurement system with the designed spatiotemporal gait analysis algorithm is a promising tool for automatically analyzing spatiotemporal gait information, serving as clinical indicators for monitoring therapeutic efficacy for diagnosis of stroke or Parkinson’s disease.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

Online Turn Fault Detection of Interior Permanent-Magnet Machines Using the Pulsating-Type Voltage Injection

This paper proposes a stator winding fault detection for interior permanent-magnet (PM) machines based on the pulsating-type voltage injection. By superimposing a pulsating voltage on d-axis with respect to the rotor-referred synchronous frame, measurable winding fault reflected signals occur in the injection induced current ripples. Comparing to prior diagnostic techniques, the proposed injection method is immune to the rotor saliency because saliency reflected current harmonic is decoupled once the pulsating voltage is exactly injected on rotor d-axis. It results in the improved fault detection performance on interior PM (IPM) machines as well as other salient machines. An IPM machine with three sets of turn-to-turn short is built to verify the proposed fault detection method.

Full Speed Region Sensorless Drive of Permanent-Magnet Machine Combining Saliency-Based and Back-EMF-Based Drive

This paper aims to improve the full speed sensorless drive for permanent-magnet (PM) synchronous machines on the region of initial zero speed and the region of medium speed. At zero and low speed, the saliency-based drive using the square-wave voltage injection has demonstrated the comparable performance to the sensor-based drive using the encoder with hundreds of pulses per revolution. For PM machines at initial state, the magnet polarity is typically identified based on the injection of voltage pulses before the saliency-based drive. Instead of pulses injection, this paper directly adds the polarity detection capability in the saliency-based drive by superimposing the same square-wave voltage. A fast detection period and negligible current spikes are benefits for the drive using the same square-wave injection voltage for both the polarity detection and position estimation. At medium speed, electromotive force voltage can be estimated to replace the saliency for the sensorless drive. To achieve a smooth operation between two estimation methods, a transition algorithm is also proposed based on a single-phase-lock loop by blending two feedback position signals. According to the experimental evaluation, the reduced power consumption as well as the smooth switch between two different drives is achieved. All the sensorless methods are verified by an interior PM machine with a saliency ratio of 1.37 (Lq/Ld = 1.37).

Online Stator Turn Fault Detection for Inverter-Fed Electric Machines Using Neutral Point Voltages Difference

This paper proposes a stator turn-to-turn fault detection for inverter-fed electric machines using the voltage difference between two neutral points (NPs). By designing machine Y-connected windings with a secondary NP, a voltage difference between two NPs appears once a turn fault occurs. Because this fault signal is induced by the voltage difference between two independent windings, only the turn fault reflected signal is resultant. It leads to an important improvement on the fault detection of electric machines since NP voltages difference is immune to the influence of saliency and flux saturation. In addition, the load and speed affect the signal level and frequency, but not the detection accuracy. It is possible to monitor a small turn fault during the machine real-time operation. A 1-kw permanent magnet machine and a 200-W induction machine with proposed two independent Y-connected windings are tested for the experimental evaluation.

On-line stator turn fault detection for inverter-fed electric machines using neutral point voltage differences

This paper proposes a stator turn-to-turn fault detection for inverter-fed electric machines using the voltage difference between two neutral points (NPs). By designing machine Y-connected windings with a secondary neutral point (NP), a voltage difference between two NPs appears once a turn fault occurs. Because this fault signal is induced by the voltage difference between two independent windings, only the turn fault reflected signal is resultant. It leads to an important improvement on the fault detection of standard electric machines since NP voltage difference is immune to the influence of saliency and saturation. In addition, the load and speed affect the signal level and frequency but not the diagnostic accuracy. It is possible to monitor a minor turn fault for the operation at zero and very low speed. A 200-W induction machine with designed two independent Y-connected windings is used for the experimental evaluation.

Development of a smart home system based on multi-sensor data fusion technology

The objective of this study is to develop a multi-sensor data fusion technology based smart home system and its intelligent monitoring interface. The functions of this system include: 1) smart entertainment and household appliances remote control; 2) indoor positioning and intelligent energy management; and 3) home fire prevention. In addition, an intelligent monitoring interface is developed for the smart home system to provide indoor environmental temperature, CO concentration, communicative environmental alarm, household appliances situation, human motion signals, and the results of gesture classification and indoor positioning in real-time. Finally, the abovementioned functions and intelligent monitoring interface are implemented in an experimental model for validating the effectiveness of the smart home system. We have executed the following three research subjects to accomplish the aforementioned objectives: 1) Develop a wearable sensing device placed on hand and its 3D gesture recognition algorithm to implement a real-time, convenient, and low-cost household appliances remote control system. 2) Develop a wearable sensing device placed on foot and its indoor pedestrian positioning algorithm to realize an effective pedestrian navigation system for smart energy management. 3) Develop a multi-sensor circuit and its intelligent fire prevention and alarm algorithm to realize an intelligent fire prevention and alarm system for home fire prevention. The experimental results have successfully validated the effectiveness of the proposed smart home system and its intelligent monitoring interface.

High-Speed Position-Sensorless Drive of Permanent-Magnet Machine Using Discrete-Time EMF Estimation

This paper proposes a permanent-magnet (PM) machine position-sensorless drive for high speed (> 10-kr/min) applications. A discrete-time back electromotive force (EMF) voltage estimation method is developed to overcome several high-speed position sensing issues, including the voltage error due to the digital-to-analog conversion, inductive cross-coupling, and deviation between the estimated EMF and actual EMF resulting from the digital implementation. To overcome these problems, an observer-based EMF estimation is proposed with three implementation considerations. They are 1) a discrete-time dq current observer to remove the inductive cross-coupling effect for the EMF estimation, 2) a delay model to compensate the voltage error, and 3) a discretized model to estimate the sampled EMF in the discrete-time. A high-speed PM machine is experimentally evaluated to verify all proposed sensorless drive methods.

Design Consideration on the Square-Wave Voltage Injection for Sensorless Drive of Interior Permanent-Magnet Machines

Although it is widely known that the saliency-based position-sensorless drive is able to achieve the closed-loop control at zero and low speed, there is little literature addressing the consideration on the selection of injection voltage frequency. This paper evaluates the square-wave injection voltage at different frequencies for the design of an interior permanent-magnet (PM) machine sensorless drive. It is shown that more flux saturation on high-frequency (HF) d-axis inductance occurs than the saturation on q-axis inductance due to the magnetic relaxation. The HF saliency ratio (Lqhf/Ldhf) is increased by increasing the injection frequency. The performance of a saliency-based sensorless drive can be enhanced by properly designing the frequency of injection voltage. This paper also includes the experimental comparison between closed-loop encoder-based and closed-loop saliency-based sensorless operation of an interior PM machine drive.

On-Line Stator Open-Phase Fault Detection and Tolerant Control for Permanent Magnet Machines Using the Neutral Point Voltage

Conventional machine stator open-phase fault detection methods rely on the detection of current harmonics from the sensing of phase currents. Because the magnitudes of current harmonics are proportional to the machine load condition, it is a challenge for the phase fault detection at the light load condition when fault-induced current harmonics are too small to detect. This paper proposes an improved stator phase fault detection for permanent magnet (PM) machines based on the use of neutral point (NP) in Y-connected windings. It is shown that the first-order voltage harmonic is resultant once the open-phase fault occurs. Comparing with prior detection methods based on the current measurement, the fault detection using NP voltage is insensitive to the load condition, because the proposed fault signal is induced by the voltage unbalance as a result of the open phase. In addition, considering the fault tolerant control, a single-phase drive is developed by connecting the machine NP to one of the inverter legs. The machine can drive at the most efficient condition using standard three-leg inverters under phase fault. A 50-W PM machine with the accessible NP is used to evaluate the proposed open-phase fault detection and tolerant control method.