• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    TinyML-Based Fall Detection for Connected Personal Mobility Vehicles

    2022-08-24 03:31:14RamonSanchezIborraLuisBernalEscobedoJoseSantaandAntonioSkarmeta
    Computers Materials&Continua 2022年5期

    Ramon Sanchez-Iborra,Luis Bernal-Escobedo,Jose Santaand Antonio Skarmeta

    1University Center of Defense,General Air Force Academy,San Javier,30720,Spain

    2University of Murcia,Murcia,30100,Spain

    3Technical University of Cartagena,Cartagena,30202,Spain

    Abstract: A new wave of electric vehicles for personal mobility is currently crowding public spaces.They offer a sustainable and efficient way of getting around in urban environments,however,these devices bring additional safety issues,including serious accidents for riders.Thereby,taking advantage of a connected personal mobility vehicle, we present a novel on-device Machine Learning(ML)-based fall detection system that analyzes data captured from a range of sensors integrated on an on-board unit (OBU) prototype.Given the typical processing limitations of these elements, we exploit the potential of the TinyML paradigm,which enables embedding powerful ML algorithms in constrained units.We have generated and publicly released a large dataset,including real riding measurements and realistically simulated falling events,which has been employed to produce different TinyML models.The attained results show the good operation of the system to detect falls efficiently using embedded OBUs.The considered algorithms have been successfully tested on mass-market low-power units, implying reduced energy consumption, flash footprints and running times, enabling new possibilities for this kind of vehicles.

    Keywords: Personal mobility;machine learning;on-board unit;C-ITS;IoT

    1 Introduction

    The arrival of eco-efficient mobility solutions is a reality in both dense urban areas and city outskirts[1].Electric devices such as mopeds,segways,or scooters are currently massively employed for both transportation and leisure activities (Fig.1).Nevertheless, the integration of these vehicles within the Cooperative-Intelligent Transportation Systems (C-ITS) ecosystem is far from being accomplished.This would enable the development of novel services and applications devoted to this specific vehicle segment, e.g., device monitoring, route planning, or traffic safety, among others.Regarding the latter,the number of accidents in which personal mobility devices are involved is notably increasing every year[2].Key statements such as per 100,000 e-scooter trips,20 people will end up with some sort of injury[3],evidence the importance of this new type of traffic accidents.This may be highly dangerous for riders, as they can suffer severe falls [4].Recent studies reveal that these vehicles are proner to falls than regular bicycles,due to body dynamics and wheel size[5].According to a recent study in [6], the type of injuries mainly involves the head (52%), arms (40%) and legs (20%), which indicate that these accidents can involve critical consequences.In fact,22.5%of the injures types were identified as fractures.Currently, these vehicles are not provided with devices detecting any kind of accident and their connection with an external system is limited to a tracking system when they are rented.In this line, from the authors’perspective, automatic fall detection (AFD) systems may be critically useful for noticing these events and triggering an alarm to the corresponding emergency service using wireless communications.

    Figure 1:Personal mobility devices ecosystem in urban environments supported by mobile computing devices

    On-board units (OBUs) are processing elements that are usually attached to vehicles aiming at enriching them with computing capabilities.This permits to run applications for supporting driving tasks and enhancing user experience[7].OBUs embedded in traditional vehicles(i.e.,cars or vans)do not usually present neither computation nor energy constraints, as they are powered by the vehicle battery.However, OBUs attached to personal mobility vehicles suffer from energy limitations, since they can have a built-in battery or be connected to the limited vehicle’s power source.For that reason,both processing and memory capacities should be carefully saved in this case.This is the reason why hardware embedded in these units and application/system software should be designed to maintain a low operation profile.This is an issue for artificial intelligent algorithms needed by smart AFD systems, which are based on complex Machine Learning (ML) techniques requiring intensive data processing.

    At this point is where the TinyML approach emerges[8].This novel paradigm proposes to adapt ML models generated by widely-adopted frameworks such as TensorFlow or Scikit-learn, aiming at making them operational on constrained end-devices.Several alternatives are already publicly available in the market, e.g., Google’s TensorFlow Lite or Microsoft’s Embedded Learning Library(ELL), among many others.In this work, we propose an AFD mechanism to be embedded in an energy-constrained OBU.To this end,firstly,a large dataset has been generated,which includes data collected from real e-scooter rides, including provoked fall events.This dataset has been employed for training a set of ML algorithms,which have been integrated within an embedded OBU prototype following the TinyML paradigm for enabling on-device inference.Once a fall event has been detected,an early alert may be triggered by means of reliable and long-range communication interfaces such as those making use of Low Power Wide Area Network(LPWAN)technologies[9].Therefore,the main contributions of this paper are the following:

    ·A public dataset for automatic detection of fall events in personal mobility devices.

    ·A discussion focused on the TinyML paradigm and its application to the sustainable mobility ecosystem.

    ·An ML-based fall detection system for personal mobility devices.

    ·A detailed study in terms of accuracy and footprint of a series of TinyML mechanisms integrated within a real OBU.

    The contents of the paper are distributed as follows.Section 2 frames the work in the literature.Section 3 discusses the applicability and benefits of TinyML for providing smart services in the field of personal mobility vehicles.Section 4 describes the reference OBU used to gather data to generate ML models and later perform the evaluation.The TinyML-based fall detection system is explained in Section 5.The attained results are presented and discussed in Section 6.Finally,the paper is concluded in Section 7,summarizing the main findings and indicating future directions.

    2 Background

    The application of optimized ML-based solutions within the scope of eco-efficient mobility has been scarcely studied in the related literature.Given that we present a real implementation of an ondevice fall detection system,in the following we explore the fields of human activity and fall detection and the use of embedded ML mechanisms for classification tasks.

    2.1 Detection of Human Activity and Falls

    Human Activity Recognition (HAR) and fall detection have been hot research topics since the advent of the IoT together with the integration of different types of low-cost sensors within smartphones and other everyday gadgets.Work in [10], focused on elderly people monitoring, proposed using floor vibrations and sound to detect falls.The presented system uses signal processing and a pattern recognition algorithm to discriminate between falls and other events, gathering training data by mimicking falls using human dolls.In turn, more recent works made use of smart-phone’s embedded sensors.For example,authors of[11]employed a simple algorithm employing acceleration measurements for detecting falls as well as long lie periods in order to reduce false positives.Work in [12] presented a more sophisticated detection method based on different ML models employing accelerometer data as well, hence demonstrating the usefulness of these low-cost and widespread smart-phone sensors for HAR and fall detection applications.Another approach using two independent devices for fall detection can be found in [13].In this case, authors made use of a smart-phone and smart-watch for simultaneously tracking the status of the user by means of their embedded accelerometers and gyroscopes.The results showed a clear decrease on false positives while maintaining the effectiveness of the detection decisions.Other works in this field focused on video and image processing can be found in the survey work presented in[14].

    In[15],authors demonstrated that model personalization,for example by using similarity between people related to physical aspects,notably improves the accuracy of HAR systems.Thus,a thorough study of the available data and user features is crucial for obtaining satisfactory results.Hence, the dataset is a key piece aiming at recognizing different patterns and situations.In the use-case under study in this work,obtaining a realistic dataset may be dangerous or hardly reproducible under laboratory conditions.Although different public and comprehensive HAR datasets can be found,e.g.,[16],they focus on daily-live movement traces and not on riding activities.To tackle this issue,we have generated our own dataset collected from real rides (including falls) in order to produce different optimized and well-adjusted models for detecting falls in the specific use-case of electric personal mobility.As explained later,this dataset is publicly available for contributing to the HAR,ITS,and IoT research communities.

    2.2 Embedded ML

    ML is an area of high importance nowadays for the IoT ecosystem, given that many consumer applications can benefit from built-in advanced computing capabilities as they permit processing sensor data directly on the end-device.This leads to energy saving in communications and a reduction in application latency[17].The integration of ML algorithms within IoT constrained elements implies extending the edge computing concept to the end-device/user itself,leading to the fog and mist computing paradigms[18].However,ML-based mechanisms usually imply intensive computing tasks and,so far,they have required their execution on high-performance computers or clusters,sometimes aided by high-throughput GPU computing or application-specific integrated circuits(ASICs).As detailed in the following section, the TinyML paradigm permits to embed intelligence (ML mechanisms) in resource-constrained end-devices, hence, opening a plethora of opportunities for developing truly intelligent IoT units.Given the novelty of this concept[8],there is not any related literature addressing the application of TinyML models to the HAR or fall detection problems.However, some efforts proposing ML-based solutions to tackle these issues have been done,as these mechanisms have shown better performance than simpler threshold-based algorithms[19].

    Work in [20] evaluated a set of ML classification algorithms, namely, k-Nearest Neighbors (k-NN, Naive Bayes, Artificial Neural Network (ANN), Support Vector Machine (SVM), and Least Squares Method (LSM), for the task of fall detection.Good accuracy over 85% was attained when distinguishing between daily activities and fall events,but authors did not present any results in terms of computing performance or memory footprint.Another approach can be found in [21], where an accident detection and classification system was developed by using data gathered from smart-phone’s built-in sensors.Here a set of ML algorithms were implemented and evaluated within the smart-phone with good accuracy results,leveraging the superior processing power of these devices in comparison with common IoT units.Work in [22] presented a fuzzy inference system that, by gathering data collected from multiple sensors in an IoT environment,is able to infer that a fall has occurred.Authors emphasized the reliability of the solution given that multiple information sources are employed.Given the great range of available options to tackle the fall detection problem with ML-based solutions,works in[23,24]deeply explored the performance of different algorithms’and models’configurations in terms of accuracy but obviating a thorough evaluation of the computation and memory impact of the considered models.

    Although additional works addressing ML-based automated fall and accident detection can be found in the literature [25], none of them permits to obtain an optimized mechanism that could be executed by an independent resource-constrained processing device.This is a critical aspect for our studied use-case,as the intelligent unit should be an embeddable solution for light and small vehicles such as electric scooters or segways not requiring external computation support.Thus, we propose to adopt the TinyML paradigm to build an intelligent and independent unit, capable of collecting the necessary data as well as conducting on-device ML inference for fall event detection.Besides,we also provide a comprehensive comparison of different TinyML models’footprint, performance and accuracy,extracted from a real proof of concept framed within the personal urban mobility ecosystem.

    3 Smart Personal Mobility Vehicles and Machine Learning

    The adoption of electric mobility solutions in our daily lives is a reality thanks to the great range of benefits brought by these devices.On the one hand, eco-efficient vehicles can replace traditional transportation means in cities due to their reduced carbon footprint as well as their mobility flexibility in crowded scenarios.Recent access restrictions to the city center in Europe, such as the cases of London,Paris or Madrid,for regular combustion vehicles,lead to the adoption of sustainable vehicles that complement regular bicycles.In addition, devices such as electric scooters and bikes, segways,etc.are also being employed for outdoor recreational activities.As it has been done for regular vehicles,the attachment of OBUs to personal mobility vehicles will allow their integration within the C-ITS ecosystem.This will boost the development of novel services for this vehicle segment.Besides,embedding intelligence in these processing units will make them evolve from simply connected elements to truly smart“things”,hence improving the eco-mobility experience and riders safety.To this end,the TinyML paradigm will fill the gap that currently hinders the implementation of ML-based mechanisms within these constrained computing platforms.

    3.1 TinyML

    The recent emergence of the TinyML paradigm [8] has enabled the integration of powerful ML schemes within constrained processing devices such as those powered by microcontrollers [26].Although previous advances have been achieved in this field, the attained solutions are usually adhoc implementations addressing specific use-cases for a given processing board,which clearly limits the generalization capability of this approach.As depicted in Fig.2,TinyML allows to convert ML models generated by well-known toolkits,e.g.,TensorFlow,Keras,PyTorch,Scikit-learn,etc.,in order to make them executable in constrained platforms.Once the pre-trained model is deployed on the resource-limited unit,the ML algorithm can be executed to cover the target use case.

    From a technical perspective,embedding intelligence in end-devices will bring interesting features to IoT deployments.Firstly,current constrained devices notably depend on their connection with the cloud in order to send collected data and/or receive commands.As communication activities can be more energy demanding that moderate processing tasks in these nodes,performing local computation for making smart decisions can be more adequate than relying on remote processing.This aspect is not negligible given that a great part of IoT devices is powered by batteries.Besides,reducing raw data transmissions further diminish cybersecurity risks and reduce computing impact of cryptography operations with their associated energy consumption.

    Figure 2:Machine learning conversion workflow to pass from a general-purpose model to a TinyML model for constrained devices

    Some TinyML frameworks are already publicly available in the market.The interest of technological companies in this paradigm is clear,as Google,Microsoft,or ARM have released different toolkits.Google’s TensorFlow Lite and Microsoft’s ELL permit to transform Deep Learning (DL) models from TensorFlow and the Microsoft Cognitive Toolkit(CNTK),respectively,to C++code that can be compiled for 32-bit processors such as the ARM Cortex-M series.Devoted to this processor as well,ARM has released the Cortex Microcontroller Software Interface Standard-NN(CMSIS-NN)framework,which is interoperable with models from TensorFlow and Caffe.Besides,ARM has also developed the ARM-NN library,which widens the range of supported boards towards ML-oriented Ethos neural processing units such as ARM Mali GPUs and ARM Cortex-A CPUs.

    As shown in Tab.1, others interesting TinyML frameworks are also available for enabling interoperability with additional ML libraries and constrained boards.For example,emlearn1https://github.com/emlearn/emlearnpermits to port a set of ML models, namely, Multi-Layer Perceptron (MLP), Decision Tree (DT), Random Forest (RF), or Gaussian Naive Bayes (GNB) generated by Scikit-learn or Keras, for making them runnable in 8-bit platforms such as the widely-adopted Arduino Uno.Also compatible with this development board, the MicroMLGen framework ports Support Vector Machine (SVM) models produced by Scikit-learn.As a result,a variety of models generated by robust ML libraries are now at our disposal to be integrated within constrained processing units.This will foster the development of a vibrant TinyML community,which will grow until forming a rich and productive ecosystem.

    In the light of the previous discussion,the TinyML paradigm,driven by the available frameworks,will permit to turn current end-devices on smart elements, therefore evolving the traditional IoT landscape to the novel Internet of Intelligent Things (IoIT).Thanks to this revolution, a new wave of applications will be designed and implemented.In the following we explore the range of potential services to be developed in the area of sustainable personal mobility.

    Table 1:TinyML frameworks reviewed

    3.2 Enabled Services

    Traffic safety will be one of the most benefited fields of application with the integration of intelligent schemes in constrained OBUs.One of the most critical services within this scope is automatic accident detection, which is deeply investigated in the following sections.A quick detection of falls is crucial for a fast reaction in order to notify the event and the accident location to the closest emergency service.Although some efforts have been devoted to the development of AFD systems for pedestrians[27]and bike riders[28],the new range of electric personal mobility vehicles has not been investigated so far, as studies such as the one in [29] reveal.Besides this application, the intelligent interconnection of personal mobility devices to the C-ITS ecosystem will boost the development of vehicle-to-vehicle (V2 V) collision avoidance systems such as those proposed for two-wheelers[30], and the implementation of advanced interaction schemes of personal vehicles with the traffic infrastructure[31].

    Another area of application is on-board intelligent vehicle diagnosis [32].By introducing ML mechanisms that could inspect different vehicle parameters,it will be able to perform self-inspections for prompt fault detection.This feature will permit to prevent mechanical failures and auto-adjust the device in case of software-solvable issues.In this line, intelligent device status tracking will be also enabled;hence,vehicle telemetry will be accessible for analysis from users’devices such as smartphones or tablets.Offline route planning is another interesting service for providing riders with tailored routes according to learned habits,cached digital maps,and special characteristics of personal mobility vehicles.Thus, this application could indicate, for example, green tracks or alternative safer routes that avoid dense traffic situations.Once parked, intelligent services related to device security will be powered by ML mechanisms.A great number of these vehicles are stolen every day, therefore the implementation of intelligent e-locks relaying on user’s biometric data will be of great interest.Traditional mechanical locks will be replaced by smart systems that may block the device upon manipulation.Besides, real-time alerts can be triggered to the vehicle’s owner if a steal-attempt is detected.

    Advanced real-time scenario monitoring will be also useful for warning riders against unhealthy pollution conditions.Continuous city sensing will help smart city platforms to collect pre-processed data from streets.Hence,a plethora of recommendation services will be enabled for improving citizens well-being,at the same time that communication resources and battery are saved.Finally,aligned with the recreational perspective of these vehicles,smart infotainment applications will be also powered by the use of ML,e.g.,music recommendations,audio guides and books,or contextual advice.

    4 Reference on-Board Unit

    In previous work carried out by authors, a functional and efficient OBU for personal mobility vehicles was developed [1].This OBU has been considered in this work to initially get training data and later validate and test the proposed TinyML-based AFD system.Its main features are depicted in Fig.3.It is a low-power consumption device provided with an off-the-shelf single board computer(SBC)equipped with Low-Power Wide-Area Network(LPWAN)capabilities.Concretely,Long Range Wide-Area Network(LoRaWAN)and Narrow-Band IoT(NB-IoT)technologies are used for the sake of minimizing energy consumption and obtaining long-range and reliable communication links.Using either LoRaWAN or NB-IoT,the OBU can access cloud services through the Internet,which can feed web applications or smart-phone apps,for instance,offering Cooperative,Connected and Automated Mobility(CCAM)advances.

    Figure 3:Architecture and prototype of the OBU used for e-scooters.(a) OBU architecture design with sensors, processing unit, basic I/O and communication transceivers (b) Functional prototype developed for validation and testing purposes

    The prototype implemented (right side of Fig.3) includes a Raspberry Pi Zero W SBC, with a System on Chip(SoC)powered by a 1 GHz ARM11 CPU coupled with 512 MB of SDRAM.The top white part is a communication board integrating a GPS receiver, a LoRaWAN transceiver (Murata CMWX1ZZABZ-093)and a NB-IoT modem(Quectel BC95).A 5 Ah lithium battery(RS PRO PBA5200)is used to reach running times over 48 h reporting data continuously.The unit case has been manufactured using a 3D printer and its dimensions are 15×7×5 cm.Among the various sensors installed in the unit,the most important one in the studied use case is the 9 degrees of freedom(DOF)inertial unit ST LSM9DS1.It includes a three-axis accelerometer, a three-axis gyroscope and threeaxis magnetometer.Hence,it provides angular velocity,acceleration and heading,which are the input of the developed AFD system.

    Given the wide adoption of Raspberry solutions in embedded electronics and,particularly,when an agile prototyping is necessary in research tasks in Academia,we have opted for a Pi Zero platform.It provides good performances under low power consumption,good I/O capabilities and the market is plenty of sensory solutions for Raspberry.Additionally,it is able to execute an embedded operating system,which makes it convenient to deploy several applications and,in our case,different TinyML models.Although microcontroller-based designs could be developed to run services and further reduce energy consumption,their flexibility for software upgrading and multi-task purposes would be much more limited.

    5 TinyML-Based Fall Detection

    This section presents a specific TinyML demonstrator in which we address the AFD issue in personal mobility vehicles by developing different ML models that are adapted to constrained devices.In our particular evaluation, we consider the OBU described above to perform an experimental evaluation of the solution.

    5.1 Dataset

    The first step to develop the proposed AFD system is creating a rich and large dataset for training and testing the generated models.To this end, a sampling campaign has been carried out with the reference OBU recording about three hours of urban e-scooter riding and 50 provoked falls in a controlled environment, including left and right overturns and front collisions at moderate speed.Fig.4 includes real photos taken during the trials, for the cases of left falls and forward collision.It is indicated the initial position of the scooter,the direction of the movement and its final status after the trial.For the forward collision trials,the scooter was manually propelled at 10 km/h to collide with a mat.The scooter used was an Outsider E-Volution 8,5 Phoenix, from Cecotec.The user was the same along the regular sampling process,covering different road types and speed/turning conditions.All trials have been carried out in the city center of Cartagena(Spain),driving on regular roads and cycle lanes.

    Data were recorded from the inertial sensor unit integrated in the OBU in order to obtain threeaxis acceleration(axi,ayi,azi)and angular velocity(θxi,θyi,θzi)at a sampling rate of 10 Hz.Considering these features,the obtained raw data vector presents six elements.We have considered the three axis components given that falls in personal mobility vehicles may happen in any direction[5].After the sampling campaign,the obtained dataset was examined and missing or corrupted samples were fixed.For the sake of experiment repeatability and reproducibility as well as for enabling further related studies,the resulting dataset has been publicly released8https://gitlab.com/scooter-project/ml-dataset.

    Besides, aiming at evaluating the performance of the considered models with different input feature vectors,we also compute the corresponding signal magnitude vector(SMV)of both monitored parameters as indicated in Eqs.(1)and(2).

    Figure 4:Procedure followed to obtain dynamics data from falls with the e-scooter.(a) Fall to left,initial status(b)Fall to left,final status(c)Frontal collision,initial status(d)Frontal collision,final status

    Given that captured measurements may suffer isolated sudden changes due to shocks caused by speed bumps or road imperfections,we finally aggregate every second,i.e.,10 samples,the captured raw data and the corresponding computed SMVs and calculate their variance.Thus,the final feature vectors that are passed to the ML algorithms are the following:(σ2(axi),σ2(ayi),σ2(azi),σ2(θxi),σ2(θyi),σ2(θzi))or(σ2(ai),σ2(θtilt i)),depending on the selected size of the input vector,namely,six or two elements,respectively.With these input vectors,the decision algorithm outputs if a fall detected or not.

    5.2 TinyML Models

    In order to evaluate the adequacy of different ML algorithms for the AFD task,we have followed the workflow of Fig.2 previously described.We have made use of the well-known Scikit-learn toolkit9https://scikit-learn.orgto train and generate a variety of ML models,namely,Support Vector Machine(SVM),Multi-Layer Perceptron (MLP), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Random Forest (RF).Given the processing and memory constraints of the OBU presented above, these models have been optimized by using different TinyML frameworks.Concretely,MicroMLGen has been used to adapt the SVM models, while the versatile emlearn library has been employed to port the rest of models generated by Scikit-learn.The dataset generated as described above has been split using stratification into three subsets:70% for training and 20% for validation in Scikit-learn, and 10% for testing in the OBU.As performance metrics,we have chosen the accuracy level of the generated models,their memory footprint,the required processing time and energy consumed once deployed in the OBU.

    5.3 Working Flow

    The overall operation of the system using any of the adopted TinyML models is depicted in the flow chart included in Fig.5.After beginning the ride,the software starts collecting data from inertial sensors continuously at a sampling rate of 10 Hz,as aforementioned.Once the samples collected fill the reference window size(10 samples in our experiments),data are aggregated to be processed by the TinyML model.This module is in charge of detecting a fall.If this is not the case,a new data collection cycle is started;whereas if a fall is detected,an alarm message is transmitted using the communication module of the OBU towards a given alert management system in the cloud.Under normal operation with no falls,the system would be running and continuously gathering data and executing the TinyMLbased algorithm in a continuous cycle.

    Figure 5:Flow chart of the operation of the TinyML-based fall-detection solution

    6 Results

    6.1 Overall Comparison of TinyML Models

    The obtained results in terms of the identified figures of merit are shown in Tab.2.Different configurations have been tested for each of the algorithms although we only present the most adequate ones for the sake of clarity.As can be seen in the last column, the accuracy achieved by all the considered algorithms is similar and their performance is notably successful(around 0,96).For most of the cases,it can be seen that the computing time is noticeably low,in the order of tens of microseconds,except for the case of the SVM, which is the slowest algorithm.This is explained by its greater complexity in comparison with the other algorithms,also evidenced by the size of the generated binary file due to the kernels are defined as float random variables.SVM also provides the lowest accuracy levels when using the Sigmoid kernel,so its usage is not recommended.

    It is noticeable the high accuracy offered by RF(0,97)when using five estimators and six input features, and requiring an execution time of only 11±14 μs.Regarding MLP models, the different evaluated network configurations provide similar accuracy levels, although the processing time and the required RAM increase with the number of neurons forming the MLP architecture because,differently from the SVM implementation,these values are stored in volatile memory.Finally,GNB presents greater accuracy than DT but longer processing times with both two or six input features.As mentioned above,the accuracy detecting falls of all of the evaluated algorithms is remarkable.

    Aiming at providing insights regarding the validity of the results,the F1-score attained for each model is also included in Tab.2.Observe that the obtained F1 scores are aligned with the accuracy values,hence indicating the good performance of the models avoiding false positives or negatives.This is of prominent importance in the application under study given that a non-detected fall may lead to serious issues for the injured driver.

    Table 2:Execution time,on-device footprint,accuracy,and F1-score of the evaluated TinyML models

    Table 2:Continued

    Regarding the footprint of the implementations on the target processing board, i.e., Raspberry Pi Zero in our case,it can be seen the low impact on both RAM and flash memories,hence making feasible to run all the tested ML classifiers with real time sensor data on such a constrained OBU.The moderate RAM required permits the generated models to coexist with other simultaneous functions running in the unit.Besides,the limited size of the binary files built for the target platform makes them suitable to be embedded in the considered unit as well as other more resource-limited boards, e.g.,microcontroller-based architectures,without the risk of consuming a significant part of the available flash memory.

    Given the relevance of power consumption in the target environment, indicative values are also given in Tab.2.They have been obtained taking as reference the consumption of the unit under 100%CPU usage together with sensor reads at a rate of 1 Hz.The value obtained after a set of consecutive execution of ML algorithms was 294,17±14,49 mA, not observing differences when executing the different models.Hence,considering the execution time of each model,it has been computed the power consumption(in nanoamperes per hour-nAh).Results in Tab.2 showcase the limited energy needed by most of the cases except for SVM,due to the aforementioned algorithm complexity.

    Considering the discussion above,GNB,DT,or RF algorithms would be the most recommended options to implement the embedded fall detection mechanism, as they present very high accuracy with a reduced impact in terms of processing latency,memory requirements,and power consumption.Additionally,SVM and MLP produce slower and heavier models as compared to GNB,DT,and RF,without providing a better performance in terms of accuracy.

    6.2 Impact of Number of Input Features

    Fig.6 depicts the models’footprint and performance variation depending on the number of inputs(two or six).Concretely,we show the relative difference of the evaluated metrics when the classifiers receive feature vectors of two or six elements,taking the former as the baseline.We find that models with six inputs present an almost negligible accuracy improvement, however, this is achieved at the expense of increasing the algorithms’running time.It can be observed that in every case,models with six input features require much more processing time than their analogous models with two inputs.This fact is especially remarkable for GNB, MLP, and SVM, with more than 20% increase in the algorithm’s computation time.

    Flash memory usage especially increases with six input features in SVM.This confirms the previous discussion regarding memory requirements of support vectors.Models based on GNB and MLP do not suffer a noticeable variation in their memory footprint depending on the number of input features.In fact, all MLP models have an identical executable file size and similar RAM footprints.This is due to the binary file padding and how the code works in this implementation (by iterating through a list of neurons), so the binary files only differ in the quantity of biases and weights that describe the network, which are really small, e.g., less than one kB for a 10+10+5 neurons model and less than 100 bytes for the two neurons model.These small differences are further reduced by the executable file padding,used to align different memory blocks in accordance with the target computer architecture.Additionally,optimizing memory accesses and code makes RAM usage to be almost the same on the small networks we have used.

    Figure 6:Models’footprint and performance variation according to the number of input features(6 vs.2),taking as a reference the results obtained with two inputs

    The cases of DT and RF are worth further analysis.In both of them a decrease in the required flash memory is obtained when increasing the number of inputs,especially in the case of RF.This behavior can be explained by the fact that the six inputs models are able to find a solution with lower tree or forest complexity in comparison with models receiving feature vectors of two elements.Regarding the impact on RAM, a similar behavior is attained for RF, while a small increase in volatile memory demands is detected for DT.In the light on these and previous outcomes, together with the high accuracy achieved,we recommend the use of RF with five estimators to build a precise and efficient ML-based AFD system.

    Overall,from the obtained results it can be extracted that the TinyML paradigm opens a plethora of opportunities for integrating ML-based mechanisms on end-devices with resource limitations.Besides,the low footprint detected enables interesting features such as over the air(OTA)updates of ML models,thus offloading the heavy task of training and upgrading the ML system onto a cloud or Multi-Access Edge Computing(MEC)node;and even further distribute training tasks by exploiting federated learning capabilities while reducing communication requirements.

    7 Conclusion

    The traditional traffic landscape consisting of fuel-powered vehicles is being enriched with a plethora of eco-efficient devices that provide users with more sustainable mobility options.However,given the novelty of personal mobility vehicles such as e-scooters,segways,or electric wheels,among others, there are still different aspects related to their tracking, connectivity, and safety that should be promptly addressed.In this work, we have explored the improvement of driver safety under falls or accidents in this segment by proposing a fall detection system powered by ML.Concretely, the state-of-the-art TinyML paradigm is exploited to develop an ML-based efficient and accurate fall detection system.To this end,a riding/fall dataset has been produced(and publicly released)to build a range of TinyML models considering different algorithms,namely,RF,DT,SVM,GNB and MLP,considering a range of configurations.These data have been obtained from real trials with an electric scooter in the city of Cartagena (Spain), and they have been used for training, validation and tests.Hence, these models have been deployed on a lightweight and connected OBU prototype in order to analyze the attained accuracy when detecting a fall, as well as evaluating the models’footprint on the host processing unit.The results validate the good operation of the proposal,as the attained classifiers’performance permits to quickly and accurately detect fall events,while the limited memory requirements and energy consumption of the produced models ensure their integration in a plethora of constrained processing units.As future work,we plan to generate the TinyML models for even more limited computing boards as well as explore innovative ML paradigms such as federated learning to speed-up algorithm generation and sharing.Besides,further analysis on the sensed data may permit to extract more sophisticated outcomes such as accident type or severity.

    Funding Statement:This work has been supported by the Spanish Ministry of Science,Innovation and Universities, under the Ramon y Cajal Program (ref.RYC-2017-23823) and the projects ONOFRE 3(ref.PID2020-112675RB)and Go2Edge(ref.RED2018-102585-T);by the European Commission,under the 5G-MOBIX(ref.825496)project;and by the Spanish Ministry for the Ecological Transition and the Demographic Challenge, under the MECANO project (ref.PGE-MOVES-SING-2019-000104).Ramon Sanchez-Iborra would like to thank Prof.Sanchez-Mu?oz for his patience and valuable comments during the preparation of this paper.

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    久久热精品热| 一个人看视频在线观看www免费| 一个人免费看片子| 亚洲精品国产av蜜桃| 久久久久久久大尺度免费视频| 99热6这里只有精品| 狂野欧美白嫩少妇大欣赏| a级毛片黄视频| 少妇猛男粗大的猛烈进出视频| 久久久久人妻精品一区果冻| 国产一区亚洲一区在线观看| 一本色道久久久久久精品综合| 搡女人真爽免费视频火全软件| 精品国产乱码久久久久久小说| 欧美精品人与动牲交sv欧美| 国产成人精品久久久久久| .国产精品久久| 人人妻人人澡人人爽人人夜夜| 精品人妻在线不人妻| 亚洲三级黄色毛片| 十八禁高潮呻吟视频| 国产欧美日韩一区二区三区在线 | 老熟女久久久| 中文字幕免费在线视频6| 蜜桃国产av成人99| 国产片特级美女逼逼视频| videossex国产| 18禁在线播放成人免费| 中文精品一卡2卡3卡4更新| 草草在线视频免费看| 3wmmmm亚洲av在线观看| 欧美亚洲 丝袜 人妻 在线| 国产免费福利视频在线观看| 狠狠婷婷综合久久久久久88av| 亚洲中文av在线| 久久韩国三级中文字幕| 一级毛片 在线播放| 精品国产乱码久久久久久小说| 22中文网久久字幕| 男的添女的下面高潮视频| 国产欧美亚洲国产| 天天影视国产精品| 亚洲少妇的诱惑av| 亚洲国产欧美日韩在线播放| 亚洲精品第二区| 美女cb高潮喷水在线观看| 亚洲欧美日韩卡通动漫| 国产视频首页在线观看| 亚洲一级一片aⅴ在线观看| 精品久久久久久久久亚洲| 日本av免费视频播放| 久久久亚洲精品成人影院| 久久久久久久久久久免费av| 伊人久久精品亚洲午夜| 国产精品国产av在线观看| 高清毛片免费看| 亚洲精品乱码久久久v下载方式| 嘟嘟电影网在线观看| 精品人妻熟女av久视频| 老司机亚洲免费影院| 啦啦啦视频在线资源免费观看| 国产亚洲欧美精品永久| 99热网站在线观看| 欧美人与性动交α欧美精品济南到 | 欧美日韩国产mv在线观看视频| 日韩制服骚丝袜av| av福利片在线| 亚洲精品乱码久久久久久按摩| 汤姆久久久久久久影院中文字幕| 亚洲欧美精品自产自拍| 日本黄色日本黄色录像| 日韩成人伦理影院| 中文字幕人妻熟人妻熟丝袜美| 久久久久国产精品人妻一区二区| 久久韩国三级中文字幕| 欧美性感艳星| 亚洲国产精品一区三区| 九色成人免费人妻av| 久久人人爽人人爽人人片va| 日本wwww免费看| 亚洲精品视频女| 成年美女黄网站色视频大全免费 | 色吧在线观看| 国产熟女午夜一区二区三区 | 夜夜骑夜夜射夜夜干| 9色porny在线观看| 在线观看国产h片| 有码 亚洲区| 日韩av免费高清视频| 黑人高潮一二区| 国产精品三级大全| 精品国产乱码久久久久久小说| 日本wwww免费看| 亚洲伊人久久精品综合| 哪个播放器可以免费观看大片| 久久久亚洲精品成人影院| 免费观看av网站的网址| 日本免费在线观看一区| 天天躁夜夜躁狠狠久久av| 亚洲av中文av极速乱| 日本-黄色视频高清免费观看| 国产精品久久久久久久电影| 国产免费福利视频在线观看| 久久精品国产亚洲av涩爱| 国产精品国产av在线观看| 在线播放无遮挡| a级片在线免费高清观看视频| 少妇的逼水好多| 涩涩av久久男人的天堂| 如何舔出高潮| 欧美日韩av久久| 99久国产av精品国产电影| 五月天丁香电影| 婷婷色综合www| 男男h啪啪无遮挡| 欧美日韩国产mv在线观看视频| 精品少妇内射三级| 亚洲,一卡二卡三卡| 国产片内射在线| xxx大片免费视频| 国产精品欧美亚洲77777| 国国产精品蜜臀av免费| 日韩中文字幕视频在线看片| 五月玫瑰六月丁香| 丰满乱子伦码专区| 又粗又硬又长又爽又黄的视频| 9色porny在线观看| 中国三级夫妇交换| 国产日韩一区二区三区精品不卡 | 丰满少妇做爰视频| 国产在视频线精品| 一区二区三区四区激情视频| 最近手机中文字幕大全| 欧美 日韩 精品 国产| 欧美一级a爱片免费观看看| 国产成人一区二区在线| 日本-黄色视频高清免费观看| 这个男人来自地球电影免费观看 | 国产亚洲最大av| 久久韩国三级中文字幕| a 毛片基地| 国产乱来视频区| 国产精品99久久99久久久不卡 | 国产成人a∨麻豆精品| 五月天丁香电影| 人妻系列 视频| 精品99又大又爽又粗少妇毛片| 又黄又爽又刺激的免费视频.| 91精品三级在线观看| 精品熟女少妇av免费看| 最黄视频免费看| 国产精品欧美亚洲77777| 亚洲高清免费不卡视频| 在线免费观看不下载黄p国产| 久热久热在线精品观看| 欧美一级a爱片免费观看看| 国产白丝娇喘喷水9色精品| 中文天堂在线官网| 亚洲国产av影院在线观看| 黑人猛操日本美女一级片| 91久久精品国产一区二区成人| 男人爽女人下面视频在线观看| 嘟嘟电影网在线观看| 在线观看免费视频网站a站| 成人亚洲欧美一区二区av| 免费高清在线观看视频在线观看| 亚洲av.av天堂| 搡老乐熟女国产| 久久久久精品性色| 久久久久久久大尺度免费视频| 久久婷婷青草| 黑丝袜美女国产一区| 亚洲熟女精品中文字幕| 大码成人一级视频| 国产免费视频播放在线视频| 青春草国产在线视频| 久久精品国产a三级三级三级| 亚洲无线观看免费| 日韩人妻高清精品专区| 国产 一区精品| 一本色道久久久久久精品综合| 国产免费视频播放在线视频| 一边摸一边做爽爽视频免费| 国产亚洲精品第一综合不卡 | 欧美变态另类bdsm刘玥| av.在线天堂| 日本wwww免费看| av天堂久久9| 最近手机中文字幕大全| 亚洲精品乱码久久久v下载方式| 亚洲无线观看免费| 精品人妻偷拍中文字幕| 黑人猛操日本美女一级片| 亚洲av免费高清在线观看| 精品99又大又爽又粗少妇毛片| 欧美激情极品国产一区二区三区 | 99热全是精品| 亚洲三级黄色毛片| 亚洲一区二区三区欧美精品| 色哟哟·www| 丝袜脚勾引网站| 18+在线观看网站| 日本色播在线视频| av在线播放精品| 国产色爽女视频免费观看| 欧美精品人与动牲交sv欧美| 中文字幕最新亚洲高清| 亚洲国产成人一精品久久久| 插逼视频在线观看| 欧美国产精品一级二级三级| 亚洲精品成人av观看孕妇| 美女中出高潮动态图| 你懂的网址亚洲精品在线观看| 久久女婷五月综合色啪小说| 精品人妻一区二区三区麻豆| 伊人久久精品亚洲午夜| 男的添女的下面高潮视频| 97超视频在线观看视频| 丝袜美足系列| 国产亚洲一区二区精品| 久久毛片免费看一区二区三区| 精品熟女少妇av免费看| 99热网站在线观看| 综合色丁香网| 一本大道久久a久久精品| 久久久久久久国产电影| 亚洲av成人精品一区久久| 五月开心婷婷网| 亚洲精品亚洲一区二区| 色婷婷av一区二区三区视频| 美女xxoo啪啪120秒动态图| a级毛片黄视频| 高清毛片免费看| 色5月婷婷丁香| 国产精品一区二区在线观看99| 欧美日韩视频精品一区| 色94色欧美一区二区| 国产黄色免费在线视频| 激情五月婷婷亚洲| 国产精品国产三级国产专区5o| 在线播放无遮挡| 在线看a的网站| 亚洲国产av影院在线观看| 在线观看国产h片| 亚洲国产色片| 中文字幕免费在线视频6| 精品少妇黑人巨大在线播放| 欧美亚洲 丝袜 人妻 在线| 菩萨蛮人人尽说江南好唐韦庄| 黄片播放在线免费| 最近2019中文字幕mv第一页| 国产免费又黄又爽又色| 色网站视频免费| 成人手机av| 亚洲欧洲日产国产| 高清欧美精品videossex| 亚洲不卡免费看| 人妻制服诱惑在线中文字幕| 成人黄色视频免费在线看| 久久久国产一区二区| 国产高清有码在线观看视频| a 毛片基地| 黄色欧美视频在线观看| 成年人免费黄色播放视频| 亚洲精品视频女| 又粗又硬又长又爽又黄的视频| 热99国产精品久久久久久7| av线在线观看网站| 国产欧美亚洲国产| 在线观看免费日韩欧美大片 | 女性被躁到高潮视频| 丝瓜视频免费看黄片| 一区二区日韩欧美中文字幕 | 99热这里只有精品一区| 亚洲国产日韩一区二区| 只有这里有精品99| 一级片'在线观看视频| 美女内射精品一级片tv| 日日啪夜夜爽| 女性生殖器流出的白浆| 日韩精品有码人妻一区| 99久久综合免费| 午夜福利视频在线观看免费| 18+在线观看网站| 国产永久视频网站| 亚州av有码| 中文字幕精品免费在线观看视频 | 日日啪夜夜爽| 91精品三级在线观看| 一区二区三区精品91| 制服人妻中文乱码| 亚洲四区av| 亚洲第一av免费看| 亚洲精品日韩在线中文字幕| 久久ye,这里只有精品| 精品人妻熟女av久视频| 国产免费又黄又爽又色| 久久久亚洲精品成人影院| 国国产精品蜜臀av免费| 久久久久网色| 国产精品麻豆人妻色哟哟久久| 久久女婷五月综合色啪小说| av免费观看日本| 综合色丁香网| 亚洲国产欧美日韩在线播放| 亚洲精品一二三| 亚洲国产精品一区二区三区在线| 日韩中字成人| 久久久久人妻精品一区果冻| 国产亚洲一区二区精品| av视频免费观看在线观看| 你懂的网址亚洲精品在线观看| 丰满乱子伦码专区| 精品一区二区三区视频在线| 国产日韩欧美在线精品| 国产一区二区三区综合在线观看 | 99精国产麻豆久久婷婷| 亚洲国产精品专区欧美| 一区二区三区免费毛片| 97在线人人人人妻| 九草在线视频观看| 少妇被粗大猛烈的视频| 久久99热这里只频精品6学生| 精品人妻熟女av久视频| 亚洲国产毛片av蜜桃av| 看十八女毛片水多多多| 看非洲黑人一级黄片| 在线观看免费高清a一片| 韩国av在线不卡| 91精品三级在线观看| 欧美精品一区二区免费开放| 亚洲av电影在线观看一区二区三区| 97精品久久久久久久久久精品| 丁香六月天网| 高清午夜精品一区二区三区| 国产男女内射视频| 九色亚洲精品在线播放| 又黄又爽又刺激的免费视频.| 午夜福利在线观看免费完整高清在| 日韩三级伦理在线观看| 亚洲精品日本国产第一区| 国产亚洲精品第一综合不卡 | 不卡视频在线观看欧美| 亚洲色图 男人天堂 中文字幕 | 日本色播在线视频| 热re99久久国产66热| 999精品在线视频| 免费大片黄手机在线观看| 精品久久久久久久久亚洲| 日本爱情动作片www.在线观看| 亚洲第一av免费看| 九草在线视频观看| 欧美精品一区二区免费开放| 最近中文字幕高清免费大全6| 久久久久久久大尺度免费视频| 国产精品偷伦视频观看了| 亚洲精品av麻豆狂野| 丝瓜视频免费看黄片| 夫妻性生交免费视频一级片| 少妇丰满av| 久久青草综合色| 精品亚洲成a人片在线观看| 亚洲中文av在线| 成人国产麻豆网| 午夜福利影视在线免费观看| 精品亚洲成a人片在线观看| 高清午夜精品一区二区三区| 在线观看三级黄色| 九色亚洲精品在线播放| 久久这里有精品视频免费| a级毛片在线看网站| 久久久精品94久久精品| 亚洲欧美一区二区三区国产| 亚洲第一av免费看| 天天操日日干夜夜撸| 国产高清国产精品国产三级| 蜜桃国产av成人99| 久久女婷五月综合色啪小说| 啦啦啦在线观看免费高清www| 欧美xxxx性猛交bbbb| 麻豆成人av视频| 国产伦精品一区二区三区视频9| 好男人视频免费观看在线| 精品久久久噜噜| 一级,二级,三级黄色视频| 91精品伊人久久大香线蕉| 日本欧美视频一区| 精品人妻偷拍中文字幕| xxxhd国产人妻xxx| 91精品国产九色| 久久久久久久久久人人人人人人| 国产成人91sexporn| 亚洲国产色片| 18+在线观看网站| 成人黄色视频免费在线看| 18禁在线无遮挡免费观看视频| 久久久久久久亚洲中文字幕| 国产免费一级a男人的天堂| 成人18禁高潮啪啪吃奶动态图 | 在线观看免费视频网站a站| 亚洲精品自拍成人| 国产高清有码在线观看视频| 丁香六月天网| 大话2 男鬼变身卡| av一本久久久久| 飞空精品影院首页| 男的添女的下面高潮视频| 国产高清有码在线观看视频| 午夜福利视频在线观看免费| 欧美三级亚洲精品| 欧美少妇被猛烈插入视频| 久久久精品区二区三区| 伦理电影大哥的女人| 午夜激情久久久久久久| 国产一级毛片在线| 精品国产一区二区三区久久久樱花| 激情五月婷婷亚洲| 狠狠精品人妻久久久久久综合| 日本-黄色视频高清免费观看| 男女边吃奶边做爰视频| 久久精品久久久久久久性| 国产成人精品福利久久| 各种免费的搞黄视频| 久久久久精品性色| 女人久久www免费人成看片| 亚洲av成人精品一二三区| 久久青草综合色| 自线自在国产av| 欧美激情国产日韩精品一区| 中文欧美无线码| a级毛片免费高清观看在线播放| 久久精品夜色国产| 免费人成在线观看视频色| 国产欧美亚洲国产| 日韩一本色道免费dvd| 国产成人精品婷婷| 王馨瑶露胸无遮挡在线观看| av在线app专区| 亚洲国产av新网站| 三上悠亚av全集在线观看| 欧美精品一区二区大全| 国产高清国产精品国产三级| 亚洲精品乱码久久久v下载方式| av网站免费在线观看视频| 啦啦啦中文免费视频观看日本| 久久久精品免费免费高清| 国产熟女午夜一区二区三区 | 精品少妇黑人巨大在线播放| www.色视频.com| 免费黄色在线免费观看| 日韩,欧美,国产一区二区三区| 18禁观看日本| 美女大奶头黄色视频| 欧美 亚洲 国产 日韩一| 久久久久久久久久久免费av| 久久人人爽人人爽人人片va| 亚洲精品456在线播放app| 99国产综合亚洲精品| 国产视频首页在线观看| 国产欧美日韩一区二区三区在线 | 国产免费视频播放在线视频| 日韩视频在线欧美| 欧美日韩视频精品一区| 日韩免费高清中文字幕av| 赤兔流量卡办理| 日本vs欧美在线观看视频| 午夜激情福利司机影院| 亚洲av二区三区四区| 国产精品欧美亚洲77777| 精品99又大又爽又粗少妇毛片| 三级国产精品欧美在线观看| 久久精品久久精品一区二区三区| 国产亚洲精品第一综合不卡 | 男女啪啪激烈高潮av片| 99久久综合免费| 日日摸夜夜添夜夜添av毛片| 色吧在线观看| 在线免费观看不下载黄p国产| 成人亚洲精品一区在线观看| √禁漫天堂资源中文www| 99久久人妻综合| 天天躁夜夜躁狠狠久久av| 久久午夜福利片| 少妇的逼水好多| 国产一区二区在线观看av| 另类精品久久| 国产精品久久久久久久电影| 综合色丁香网| 嫩草影院入口| 日韩一区二区视频免费看| 日日撸夜夜添| 18禁观看日本| av在线观看视频网站免费| 色婷婷久久久亚洲欧美| 波野结衣二区三区在线| 9色porny在线观看| av专区在线播放| 久久久久久久久大av| 丝袜在线中文字幕| 国产精品 国内视频| 国产在视频线精品| 国产精品.久久久| 国产日韩一区二区三区精品不卡 | 亚洲欧美一区二区三区黑人 | 日本黄色日本黄色录像| 国产精品免费大片| 日本91视频免费播放| 视频区图区小说| 亚洲成人一二三区av| 一级,二级,三级黄色视频| 日韩亚洲欧美综合| 国产成人精品在线电影| 天堂8中文在线网| 亚洲欧美色中文字幕在线| 日本免费在线观看一区| 久久综合国产亚洲精品| 国产一区有黄有色的免费视频| 91久久精品电影网| 91久久精品国产一区二区成人| 一本一本久久a久久精品综合妖精 国产伦在线观看视频一区 | 在线观看免费高清a一片| 熟女av电影| 亚洲综合色网址| 成年av动漫网址| 国产午夜精品久久久久久一区二区三区| 大香蕉97超碰在线| 成人亚洲精品一区在线观看| 天美传媒精品一区二区| 久久精品熟女亚洲av麻豆精品| 亚洲av.av天堂| 国产深夜福利视频在线观看| av播播在线观看一区| 51国产日韩欧美| 一级毛片黄色毛片免费观看视频| 国精品久久久久久国模美| 中国美白少妇内射xxxbb| 国产精品三级大全| 久久久久久久亚洲中文字幕| 欧美日韩精品成人综合77777| 一级黄片播放器| 精品人妻偷拍中文字幕| 一本一本综合久久| 欧美三级亚洲精品| 亚洲欧美日韩卡通动漫| 99re6热这里在线精品视频| 国产精品三级大全| 一个人免费看片子| 亚洲国产精品专区欧美| 免费黄网站久久成人精品| 成人黄色视频免费在线看| 国产精品蜜桃在线观看| 国产精品一国产av| 午夜激情福利司机影院| 91久久精品电影网| 国产精品熟女久久久久浪| 免费大片黄手机在线观看| 9色porny在线观看| 亚洲欧美成人精品一区二区| 一级片'在线观看视频| 内地一区二区视频在线| 中文欧美无线码| 少妇熟女欧美另类| 久久热精品热| 下体分泌物呈黄色| 高清黄色对白视频在线免费看| 免费观看的影片在线观看| 欧美日韩综合久久久久久| 国产免费视频播放在线视频| 在线精品无人区一区二区三| 男女国产视频网站| 精品视频人人做人人爽| 国产精品国产三级国产av玫瑰| 国产色爽女视频免费观看| 国产精品国产三级国产av玫瑰| 亚洲不卡免费看| 日本wwww免费看| 欧美 日韩 精品 国产| 久久99精品国语久久久| 最近的中文字幕免费完整| 寂寞人妻少妇视频99o| 日韩欧美精品免费久久| 国产白丝娇喘喷水9色精品| 日本爱情动作片www.在线观看| 欧美+日韩+精品| 久久人人爽av亚洲精品天堂| 欧美老熟妇乱子伦牲交| 中国国产av一级| 欧美激情 高清一区二区三区| 制服人妻中文乱码| 久久这里有精品视频免费| 久久人人爽人人爽人人片va| 欧美一级a爱片免费观看看| 最近最新中文字幕免费大全7| 97在线人人人人妻| 七月丁香在线播放| h视频一区二区三区| 少妇的逼好多水| 国产精品不卡视频一区二区| 9色porny在线观看| 91精品一卡2卡3卡4卡| a级毛片免费高清观看在线播放| 亚洲精品久久成人aⅴ小说 | 日韩免费高清中文字幕av| 日本免费在线观看一区| 国产在视频线精品| 亚洲av日韩在线播放| 国产老妇伦熟女老妇高清| 欧美精品人与动牲交sv欧美| 国产黄色视频一区二区在线观看| 日韩一本色道免费dvd| 秋霞在线观看毛片| 只有这里有精品99| 久热久热在线精品观看| 99热国产这里只有精品6| 中文字幕制服av|