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Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm.
Uduak George; Kee Moon; Sung Lee. Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System. Sensors 2021, 21, 1393 .
AMA StyleUduak George, Kee Moon, Sung Lee. Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System. Sensors. 2021; 21 (4):1393.
Chicago/Turabian StyleUduak George; Kee Moon; Sung Lee. 2021. "Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System." Sensors 21, no. 4: 1393.
This paper proposes a new method to estimate the position of an object and a silent person with a home security system using a loudspeaker and an array of microphones. The conventional acoustic-based security systems have been developed to detect intruders and estimate the direction of intruders who generate noise. However, there is a need for a method to estimate the distance and angular position of a silent intruder for interoperation with the conventional security sensors, thus overcoming the disadvantage of acoustic-based home security systems, which operate only when sound is generated. Therefore, an active localization method is proposed to estimate the direction and distance of a silent person by actively detecting the sound field variation measured by the microphone array after playing the sound source in the control zone. To implement the idea of the proposed method, two main aspects were studied. Firstly, a signal processing method that estimates the position of a person by the reflected sound, and secondly, the environment in which the proposed method can be operated through a finite-difference time-domain (FDTD) simulation and the acoustic parameters of early decay time (EDT) and reverberation time (RT20). Consequently, we verified that with the proposed method it is possible to estimate the position of a polyvinyl chloride (PVC) pipe and a person by using their reflection in a classroom.
Kihyun Kim; Semyung Wang; Homin Ryu; Sung Q. Lee. Acoustic-Based Position Estimation of an Object and a Person Using Active Localization and Sound Field Analysis. Applied Sciences 2020, 10, 9090 .
AMA StyleKihyun Kim, Semyung Wang, Homin Ryu, Sung Q. Lee. Acoustic-Based Position Estimation of an Object and a Person Using Active Localization and Sound Field Analysis. Applied Sciences. 2020; 10 (24):9090.
Chicago/Turabian StyleKihyun Kim; Semyung Wang; Homin Ryu; Sung Q. Lee. 2020. "Acoustic-Based Position Estimation of an Object and a Person Using Active Localization and Sound Field Analysis." Applied Sciences 10, no. 24: 9090.
Social cognition requires neural processing, yet a unifying method linking particular brain activities and social behaviors is lacking. Here, we embedded mobile edge computing (MEC) and light emitting diodes (LEDs) on a neurotelemetry headstage, such that a particular neural event of interest is processed by the MEC and subsequently an LED is illuminated, allowing simultaneous temporospatial visualization of that neural event in multiple, socially interacting mice. As a proof of concept, we configured our system to illuminate an LED in response to gamma oscillations in the basolateral amygdala (BLA gamma) in freely moving mice. We identified (i) BLA gamma responses to a spider robot, (ii) affect-related BLA gamma during conflict, and (iii) formation of defensive aggregation under a threat by the robot, and reduction of BLA gamma responses in the inner-located mice. Our system can provide an intuitive framework for examining brain-behavior connections in various ecological situations and population structures.
Jisoo Kim; Chaewoo Kim; Hio-Been Han; Cheol Jun Cho; Wooseob Yeom; Sung Q. Lee; Jee Hyun Choi. A bird’s-eye view of brain activity in socially interacting mice through mobile edge computing (MEC). Science Advances 2020, 6, eabb9841 .
AMA StyleJisoo Kim, Chaewoo Kim, Hio-Been Han, Cheol Jun Cho, Wooseob Yeom, Sung Q. Lee, Jee Hyun Choi. A bird’s-eye view of brain activity in socially interacting mice through mobile edge computing (MEC). Science Advances. 2020; 6 (49):eabb9841.
Chicago/Turabian StyleJisoo Kim; Chaewoo Kim; Hio-Been Han; Cheol Jun Cho; Wooseob Yeom; Sung Q. Lee; Jee Hyun Choi. 2020. "A bird’s-eye view of brain activity in socially interacting mice through mobile edge computing (MEC)." Science Advances 6, no. 49: eabb9841.
Although sound source localization is a desirable technique in many communication systems and intelligence applications, the distortion caused by diffuse noise or reverberation makes the time delay estimation (TDE) between signals acquired by a pair of microphones a complicated and challenging problem. In this paper, we describe a method that can efficiently achieve sound source localization in noisy and reverberant environments. This method is based on the generalized cross-correlation (GCC) function with phase transform (PHAT) weights (GCC-PHAT) to achieve robustness against reverberation. In addition, to estimate the time delay robust to diffuse components and to further improve the robustness of the GCC-PHAT against reverberation, time-frequency(t-f) components of observations directly emitted by a point source are chosen by “inversed” diffuseness. The diffuseness that can be estimated from the coherent-to-diffuse power ratio (CDR) based on spatial coherence between two microphones represents the contribution of diffuse components on a scale of zero to one with direct sounds from a source modeled to be fully coherent. In particular, the “inversed” diffuseness is binarized with a very rigorous threshold to select highly reliable components for accurate TDE even in noisy and reverberant environments. Experimental results for both simulated and real-recorded data consistently demonstrated the robustness of the presented method against diffuse noise and reverberation.
Ran Lee; Min-Seok Kang; Bo-Hyun Kim; Kang-Ho Park; Sung Q Lee; Hyung-Min Park. Sound Source Localization Based on GCC-PHAT With Diffuseness Mask in Noisy and Reverberant Environments. IEEE Access 2020, 8, 7373 -7382.
AMA StyleRan Lee, Min-Seok Kang, Bo-Hyun Kim, Kang-Ho Park, Sung Q Lee, Hyung-Min Park. Sound Source Localization Based on GCC-PHAT With Diffuseness Mask in Noisy and Reverberant Environments. IEEE Access. 2020; 8 (99):7373-7382.
Chicago/Turabian StyleRan Lee; Min-Seok Kang; Bo-Hyun Kim; Kang-Ho Park; Sung Q Lee; Hyung-Min Park. 2020. "Sound Source Localization Based on GCC-PHAT With Diffuseness Mask in Noisy and Reverberant Environments." IEEE Access 8, no. 99: 7373-7382.
Gait signifies the walking pattern of an individual. It may be normal or abnormal, depending on the health condition of the individual. This paper considers the development of a gait sensor network system that uses a pair of wireless inertial measurement unit (IMU) sensors to monitor the gait cycle of a user. The sensor information is used for determining the normality of movement of the leg. The sensor system places the IMU sensors on one of the legs to extract the three-dimensional angular motions of the hip and knee joints while walking. The wearable sensor is custom-made at San Diego State University with wireless data transmission capability. The system enables the user to collect gait data at any site, including in a non-laboratory environment. The paper also presents the mathematical calculations to decompose movements experienced by a pair of IMUs into individual and relative three directional hip and knee joint motions. Further, a new approach of gait pattern classification based on the phase difference angles between hip and knee joints is presented. The experimental results show a potential application of the classification method in the areas of smart detection of abnormal gait patterns.
Kee S. Moon; Sung Q Lee; Yusuf Ozturk; Apoorva Gaidhani; Jeremiah A. Cox. Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network. Sensors 2019, 19, 5024 .
AMA StyleKee S. Moon, Sung Q Lee, Yusuf Ozturk, Apoorva Gaidhani, Jeremiah A. Cox. Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network. Sensors. 2019; 19 (22):5024.
Chicago/Turabian StyleKee S. Moon; Sung Q Lee; Yusuf Ozturk; Apoorva Gaidhani; Jeremiah A. Cox. 2019. "Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network." Sensors 19, no. 22: 5024.
This paper presents a novel hybrid sensor-based intrusion detection system for low-power surveillance in an empty, sealed indoor space with or without illumination. The proposed system includes three functional steps: (i) initial detection of an intrusion event using a sound field sensor; (ii) automatic lighting control based on the detected event, and (iii) detection and tracking the intruder using an image sensor. The proposed hybrid sensor-based surveillance system uses a sound field sensor to detect an abnormal event in a very low-light or completely dark environment for 24 h a day to reduce the power consumption. After detecting the intrusion by the sound sensor, a collaborative image sensor takes over an accurate detection and tracking tasks. The proposed hybrid system can be applied to various surveillance environments such as an office room after work, empty automobile, safety room in a bank, and armory room. This paper deals with fusion of computer-aided pattern recognition and physics-based sound field analysis that reflects the symmetric aspect of computer vision and physical analysis
Hasil Park; Jinho Park; Heegwang Kim; Sung Q Lee; Kang-Ho Park; Joonki Paik. Hybrid Sensor Network-Based Indoor Surveillance System for Intrusion Detection. Symmetry 2018, 10, 181 .
AMA StyleHasil Park, Jinho Park, Heegwang Kim, Sung Q Lee, Kang-Ho Park, Joonki Paik. Hybrid Sensor Network-Based Indoor Surveillance System for Intrusion Detection. Symmetry. 2018; 10 (6):181.
Chicago/Turabian StyleHasil Park; Jinho Park; Heegwang Kim; Sung Q Lee; Kang-Ho Park; Joonki Paik. 2018. "Hybrid Sensor Network-Based Indoor Surveillance System for Intrusion Detection." Symmetry 10, no. 6: 181.
Respiratory activity is an essential vital sign of life that can indicate changes in typical breathing patterns and irregular body functions such as asthma and panic attacks. Many times, there is a need to monitor breathing activity while performing day-to-day functions such as standing, bending, trunk stretching or during yoga exercises. A single IMU (inertial measurement unit) can be used in measuring respiratory motion; however, breathing motion data may be influenced by a body trunk movement that occurs while recording respiratory activity. This research employs a pair of wireless, wearable IMU sensors custom-made by the Department of Electrical Engineering at San Diego State University. After appropriate sensor placement for data collection, this research applies principles of robotics, using the Denavit-Hartenberg convention, to extract relative angular motion between the two sensors. One of the obtained relative joint angles in the “Sagittal” plane predominantly yields respiratory activity. An improvised version of the proposed method and wearable, wireless sensors can be suitable to extract respiratory information while performing sports or exercises, as they do not restrict body motion or the choice of location to gather data.
Apoorva Gaidhani; Kee S. Moon; Yusuf Ozturk; Sung Q. Lee; Woosub Youm. Extraction and Analysis of Respiratory Motion Using Wearable Inertial Sensor System during Trunk Motion. Sensors 2017, 17, 2932 .
AMA StyleApoorva Gaidhani, Kee S. Moon, Yusuf Ozturk, Sung Q. Lee, Woosub Youm. Extraction and Analysis of Respiratory Motion Using Wearable Inertial Sensor System during Trunk Motion. Sensors. 2017; 17 (12):2932.
Chicago/Turabian StyleApoorva Gaidhani; Kee S. Moon; Yusuf Ozturk; Sung Q. Lee; Woosub Youm. 2017. "Extraction and Analysis of Respiratory Motion Using Wearable Inertial Sensor System during Trunk Motion." Sensors 17, no. 12: 2932.
In this paper, we proposed a MEMS capacitive microphone with a dual-anchored membrane. The proposed dual anchor could minimize the deviation of operating characteristics of the membrane according to the fabrication process variation. The membrane is connected and fixed to the back plate insulating silicon nitride structures instead to the sacrificial bottom insulating oxide layer so that its effective size and boundary conditions are not changed according to the process variation. The proposed dual-anchored MEMS microphone is fabricated by the conventional fabrication process without no additional process and mask. It has a sensing membrane of 500 μm diameter, an air gap of 2.0 μm and 12 dual anchors of 15 μm diameter. The resonant frequency and the pull-in voltage of the fabricated device is 36.3 ± 1.3 kHz and 6.55 ± 0.20 V, respectively.
Chang Han Je; Ju Hyun Jeon; Sung Q. Lee; Woo Seok Yang. MEMS Capacitive Microphone with Dual-Anchored Membrane. Proceedings 2017, 1, 342 .
AMA StyleChang Han Je, Ju Hyun Jeon, Sung Q. Lee, Woo Seok Yang. MEMS Capacitive Microphone with Dual-Anchored Membrane. Proceedings. 2017; 1 (4):342.
Chicago/Turabian StyleChang Han Je; Ju Hyun Jeon; Sung Q. Lee; Woo Seok Yang. 2017. "MEMS Capacitive Microphone with Dual-Anchored Membrane." Proceedings 1, no. 4: 342.
All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time.
Yi Su; Sudhamayee Routhu; Kee S. Moon; Sung Q. Lee; Woosub Youm; Yusuf Ozturk. A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface. Sensors 2016, 16, 1582 .
AMA StyleYi Su, Sudhamayee Routhu, Kee S. Moon, Sung Q. Lee, Woosub Youm, Yusuf Ozturk. A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface. Sensors. 2016; 16 (10):1582.
Chicago/Turabian StyleYi Su; Sudhamayee Routhu; Kee S. Moon; Sung Q. Lee; Woosub Youm; Yusuf Ozturk. 2016. "A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface." Sensors 16, no. 10: 1582.
The MEMS (micro-electro-mechanical systems) microphone enables the manufacturing of small mechanical components on the surface of a silicon wafer. The MEMS microphones are less susceptible to vibration because of the smaller diaphragm mass and an excellent candidate for chip-scale packaging. In this paper, we present a piezoelectric MEMS microphone based on (1-x)Pb(Mg1/3Nb2/3)O3-xPbTiO3 (PMN-PT) single crystal diaphragm. The PMN-PT materials exhibit extremely high piezoelectric coefficients and other desirable properties for an acoustic sensor. The piezoelectric-based microphone can offer the ability to passively sense without the power requirements. In particular, this paper introduces the design of a PMN-PT single crystal diaphragm with interdigitated electrode. We were able to fabricate miniaturized PMN-PT single crystal diaphragms. The fabricated sensor exhibits the sensitivity of 1.5mV/Pa. This implies that the PMN-PT thin film microphone has a potential of excellent acoustic characteristics.
Sung Q Lee; Hye Jin Kim; Sang Kyun Lee; Jae Woo Lee; Kang Ho Park. PMN-PT Single Crystal Piezo-Electric Acoustic Sensor. MRS Proceedings 2007, 1034, 1 .
AMA StyleSung Q Lee, Hye Jin Kim, Sang Kyun Lee, Jae Woo Lee, Kang Ho Park. PMN-PT Single Crystal Piezo-Electric Acoustic Sensor. MRS Proceedings. 2007; 1034 ():1.
Chicago/Turabian StyleSung Q Lee; Hye Jin Kim; Sang Kyun Lee; Jae Woo Lee; Kang Ho Park. 2007. "PMN-PT Single Crystal Piezo-Electric Acoustic Sensor." MRS Proceedings 1034, no. : 1.
We have developed a miniature silicon condenser microphone improved with a spring supported hinge diaphragm and a large back volume, which is designed in order to increase sensitivity of microphones. MEMS Technology has been successfully applied to miniature silicon capacitive microphones, and we fabricated the smallest condenser silicon microphone in the presented reports. We used the finite-element analysis (FEA) to evaluate mechanical and acoustic performances of the condenser microphone with a flexure hinge diaphragm. From the simulation results, we confirmed that the sensitivity of a flexure hinge diaphragm can be improved about 285 times higher than a flat diaphragm. The first and second modes occurred at 15,637Hz and 24,387Hz, respectively. The areas of the miniature condenser microphones with a hinge diaphragm are 1.5 mm × 1.5 mm. We measured the impedance characteristics and sensitivities of the fabricated condenser microphones. The sensitivities of microphones are around 12.87 μV/Pa (-60 dB ref. 12.5 mV/Pa) at 1 kHz under a low bias voltage of 1 V, and the frequency response is flat up to 13 kHz.
Hyejin Kim; Sung Q Lee; Jaewoo Lee; Sangkyun Lee; Kangho Park. A Miniature Silicon Condenser Microphone Improved with a Flexure Hinge Diaphragm and a Large Back Volume. MRS Proceedings 2007, 1052, 1 .
AMA StyleHyejin Kim, Sung Q Lee, Jaewoo Lee, Sangkyun Lee, Kangho Park. A Miniature Silicon Condenser Microphone Improved with a Flexure Hinge Diaphragm and a Large Back Volume. MRS Proceedings. 2007; 1052 ():1.
Chicago/Turabian StyleHyejin Kim; Sung Q Lee; Jaewoo Lee; Sangkyun Lee; Kangho Park. 2007. "A Miniature Silicon Condenser Microphone Improved with a Flexure Hinge Diaphragm and a Large Back Volume." MRS Proceedings 1052, no. : 1.