Vehicle Steering Detector
有调查显示，交通事故往往是由驾驶员危险驾驶行为所引起的，及时检测这些行为并进行预警对于避免交通事故至关重要。车载诊断系统OBD(On-Board Diagnostic)和高级驾驶辅助系统ADAS(Advanced Driver Assistant System)造价昂贵，因此通常不适用于大众车型。随着智能设备的发展，智能手机的快速发展和强大的内置传感器是解决此问题的新途径。
Monitoring the Tooth Brushing
Quality for Teenagers with Smart Watch
With the increasing job stress and fast tempo of modern life, people from all walks of life have less time for their hygiene routines—the daily tooth brushing is one of them. In fact, brushing teeth every day may be the most cost-efficient way to maintain oral health, when considering the frequent occurrence of oral health problems and the high cost of consequent dental cares.
In 1988, the authors presented a smart system by modifying the holder of a toothbrush with chip that has build-in accelerometer, magnetic sensor and communication module. In 2013, Authors of Rudiger ZillmerRudiger also present a 3-axial accelerometer-based toothbrushing activity detector. In 2016, due to the non-reusable of toothbrush, Hua HuangHua and Lin, used the sensors in smart watch to capture the hand poses during tooth brushing. In 2018, Boyu proposed a smart Water Flosse that was also an IMU-based detection system, in which the researchers modified the holder of the water flosser to capture the usage of during washing the gingiva and gaps between teeth. They divided the whole mouth into 16 diﬀerent areas, and compared diﬀerent machine learning models for detecting the motions.
By adding a simple and cheap 3D colored target at the end of the toothbrush, the researchers developed a camera-based application to capture the brushing pose through a tablet or a smartphone. The added target is a colored ball with different color lumps. And then Marco updated their system by adding IMU sensors with the colored ball. By placing a small camera in the center of the Halo, some researchers designed a vision based brushing evaluator called LumiO. They used Quantitative Light-induced Fluorescence (QLF) to analyze the pictures captured inside the mouth, and detection the plaque on the tooth surface with three machine learning techniques.
Korpela et al., 2015 ﬁrst evaluated the daily tooth brushing performance with sound data. Their system utilized the normal smartphone microphone to capture the brushing signals and build hidden Markov models (HMMs) to recognize the brushing actions. Diﬀerent combination of features are used to evaluate brushing performance scores. A novel asymmetrical acoustic ﬁeld detection system Zhenchaoetal.,2017 is designed by combining a single-channel throat microphone and a single-channel bluetooth earphone. Diﬀerent time domain and frequent domain features are calculated from the raw signals for the training of ﬁve diﬀerent machine learning models. And the researcher found the random forest model can achieve the best performance.
In this paper, we present a novel tooth brushing monitoring and evaluating system by using smart devices. Our system captures the motion behavior of the user’s hand and unique acoustic signals during tooth brushing through the inertia measurement unit (IMU) sensor (accelerator and gyroscope) and a normal microphone, respectively. We also analyze the relationship between the characteristics of the signal and the corresponding brushing surface, and build several diﬀerent machine learning models in supervised learning method.
During data collection, each time the volunteer wears the smart watch and brushes their teeth for three minutes. An assistant will label the brushing event and use the smart watch App to collect IMU sensor data and microphone acoustic signal which will be transmitted to the smartphone later. The smartphone divides the sensor data into fixed windows and calculates the time and frequent domain features. Combined with the labeled brushing events, we organized a brushing event dataset for machine learning modeling.
We first divide the oral area into 6 different teeth surfaces, two front teeth and four back teeth. Each front teeth contains a inner surface and a outer surface, and each back teeth contains a inner surface, a chewing surface and a outer surface. Therefore, the oral area is total divided into 16 different areas.
The smart watch based monitoring system that can capture user’s hand motion and brushing acoustic signal thought build-in IMU sensor and microphone. The IMU are commonly used for measuring the gestures, while the microphone can capture the sound when taking phone calls.
The smart watch is used for collecting tooth brushing behaviors of the user.
Model training: The smart watch App will collect IMU sensor data and microphone acoustic signal, and transmit to the smartphone. A sliding window with fixed step is used for cutting the data into fixed frames to calculate the time and frequent domain features. Combined with the labeled brushing events, we organized a brushing event dataset for machine learning modeling. Diﬀerent models are trained to select the best candidate model.
Real-time monitoring: After the selected model is integrated into smartphone data processing ﬂow, the system can directly use the data of the total brushing time on each surface to evaluate the whole brushing quality.
If the Bass brushing method is used, the total brushing time is required to be no less than 3 minutes. The model will alerts the user when it detects that a tooth surface is cleaned for less than 10 seconds.