| 1. |
- Du, Jiaying, et al.
(författare)
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Perception of Delay in Computer Input Devices Establishing a Baseline for Signal Processing of Motion Sensor Systems
- 2016
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Ingår i: The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16. - Västeraås, Sweden : Springer International Publishing. ; , s. 107-112
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Konferensbidrag (refereegranskat)abstract
- New computer input devices in healthcare applications using small embedded sensors need firmware filters to run smoothly and to provide a better user experience. Therefore, it has to be investigated how much delay can be tolerated for signal processing before the users perceive a delay when using a computer input device. This paper is aimed to find out a threshold of unperceived delay by performing user tests with 25 participants. A communication retarder was used to create delays from 0 to 100 ms between a receiving computer and three different USB-connected computer input devices. A wired mouse, a wifi mouse and a head-mounted mouse were used as input devices. The results of the user tests show that delays up to 50ms could be tolerated and are not perceived as delay, or depending on the used device still perceived as acceptable.
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| 2. |
- Du, Jiaying
(författare)
-
Signal processing for MEMS sensor based motion analysis system
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Sensor systems for motion analysis represent an important class of embeddedsensor systems for health, and are usually based on MEMS technology(Micro-electro-mechanical systems). Gyroscopes and accelerometers are two examples of MEMS motion sensors that are characterized by their small size,low weight, low power consumption, and low cost. This makes them suitableto be used in wearable systems, intended to measure body movements and posture,and to provide the input for advanced human motion analyzes. However,MEMS-sensors usually are sensitive to environmental disturbances, such as shock, vibration and temperature changes. A large portion of the measured MEMS-sensor signal actually origins from error sources such as noise, offset, and drift. Especially, temperature drift is a well-known error source. Accumulation errors increase the effect of the error during integration of acceleration orangular rate to determine the position or angle. Thus, methods to limit or eliminate the influence of the sources of errors are urgent. Due to MEMS-sensor characteristics and the measurement environment in human motion analysis,signal processing is regarded as an important and necessary part of a MEMS-sensor based human motion analysis system.This licentiate thesis focuses on signal processing for MEMS-sensor based human motion analysis systems. Different signal processing algorithms were developed, comprising noise reduction, offset/drift estimation and reduction,position accuracy and system stability. Further, real time performance was achieved, also fulfilling the hardware requirement of limited calculation capacity.High-pass filter, LMS algorithm and Kalman filter were used to reduce offset, drift and especially temperature drift in a MEMS-gyroscope based system,while low-pass filter, LMS algorithm, Kalman filter and WFLC algorithms were used for noise reduction. Simple methods such as thresholding with delay and velocity estimation were developed to improve the signal during the position measurements. A combination method of Kalman filter, WFLC algorithm and thresholding with delay was developed with focus on the static stability and position accuracy of the MEMS-gyroscope based system. These algorithms have been implemented into a previously developed MEMS-sensorbased motion analysis system. The computational times of the algorithms were all acceptable. Kalman filtering was found efficient to reduce the problem of temperature drift and the WFLC algorithm was found the most suitable method to reduce human physiological tremor and electrical noise. With the Trapezoidal method and low-pass filter, threshold with delay method and velocity estimation method reduced integrated drift in one minute by about 20 meters for the position measurements with MEMS-accelerometer. The threshold with delay method made the signal around zero level to zero without interrupting the continuous movement signal. The combination method of Kalman filter,WFLC algorithm and threshold with delay method showed its superiority on improving the static stability and position accuracy by reducing noise, offset and drift simultaneously, 100% error reduction during the static state, 98.2%position error correction in the case of movements without drift, and 99% with drift.
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| 3. |
- Du, Jiaying, et al.
(författare)
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The effects of perceived USB-delay for sensor and embedded system development
- 2016
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBSVolume 2016. - 9781457702204 ; , s. 2492-2495
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Konferensbidrag (refereegranskat)abstract
- Perceiving delay in computer input devices is a problem which gets even more eminent when being used in healthcare applications and/or in small, embedded systems. Therefore, the amount of delay found as acceptable when using computer input devices was investigated in this paper. A device was developed to perform a benchmark test for the perception of delay. The delay can be set from 0 to 999 milliseconds (ms) between a receiving computer and an available USB-device. The USB-device can be a mouse, a keyboard or some other type of USB-connected input device. Feedback from performed user tests with 36 people form the basis for the determination of time limitations for the USB data processing in microprocessors and embedded systems without users' noticing the delay. For this paper, tests were performed with a personal computer and a common computer mouse, testing the perception of delays between 0 and 500 ms. The results of our user tests show that perceived delays up to 150 ms were acceptable and delays larger than 300 ms were not acceptable at all.
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