基于STM32单片机的智能手势识别手套的设计与应用

基于STM32单片机的智能手势识别手套的设计与应用一、本文概述Overviewofthisarticle随着物联网、和嵌入式系统技术的快速发展，人机交互方式正经历着前所未有的变革。智能手势识别手套作为一种新颖的人机交互工具，其设计和应用逐渐受到研究者和市场的关注。本文旨在探讨基于STM32单片机的智能手势识别手套的设计与应用，通过对其硬件架构、软件编程、手势识别算法以及实际应用场景等方面进行详细阐述，展示这一技术的实现过程和潜在价值。WiththerapiddevelopmentoftheInternetofThingsandembeddedsystemtechnology,human-computerinteractionmethodsareundergoingunprecedentedchanges.Asanovelhuman-computerinteractiontool,thedesignandapplicationofintelligentgesturerecognitionglovesaregraduallyreceivingattentionfromresearchersandthemarket.ThisarticleaimstoexplorethedesignandapplicationofintelligentgesturerecognitionglovesbasedontheSTM32microcontroller.Byelaboratingonitshardwarearchitecture,softwareprogramming,gesturerecognitionalgorithms,andpracticalapplicationscenarios,theimplementationprocessandpotentialvalueofthistechnologyaredemonstrated.文章首先将对智能手势识别手套的背景和意义进行介绍，阐述其在人机交互、虚拟现实、游戏娱乐、医疗康复等领域的广阔应用前景。接着，将详细介绍基于STM32单片机的智能手势识别手套的硬件设计方案，包括传感器选型、电路布局、电源管理等方面。在此基础上，文章将深入探讨手势识别算法的选择和实现，包括数据采集、预处理、特征提取和分类识别等关键步骤。Thearticlewillfirstintroducethebackgroundandsignificanceofintelligentgesturerecognitiongloves,andexplaintheirbroadapplicationprospectsinfieldssuchashuman-computerinteraction,virtualreality,gameentertainment,medicalrehabilitation,etc.Next,thehardwaredesignschemeofintelligentgesturerecognitionglovesbasedonSTM32microcontrollerwillbeintroducedindetail,includingsensorselection,circuitlayout,powermanagement,andotheraspects.Onthisbasis,thearticlewilldelveintotheselectionandimplementationofgesturerecognitionalgorithms,includingkeystepssuchasdatacollection,preprocessing,featureextraction,andclassificationrecognition.文章还将对智能手势识别手套的软件编程进行说明，包括STM32单片机的编程环境搭建、程序编写和调试过程。将结合具体的应用场景，展示智能手势识别手套在实际应用中的表现，分析其优势和局限性，并提出改进和优化建议。Thearticlewillalsoexplainthesoftwareprogrammingofintelligentgesturerecognitiongloves,includingtheprogrammingenvironmentconstruction,programwriting,anddebuggingprocessoftheSTM32microcontroller.Wewilldemonstratetheperformanceofintelligentgesturerecognitionglovesinpracticalapplications,analyzetheiradvantagesandlimitations,andproposeimprovementandoptimizationsuggestionsbasedonspecificapplicationscenarios.文章将总结基于STM32单片机的智能手势识别手套的设计与应用经验，展望其未来的发展方向和应用前景，为相关领域的研究者和实践者提供参考和借鉴。ThearticlewillsummarizethedesignandapplicationexperienceofintelligentgesturerecognitionglovesbasedonSTM32microcontroller,andlookforwardtoitsfuturedevelopmentdirectionandapplicationprospects,providingreferenceandguidanceforresearchersandpractitionersinrelatedfields.二、手势识别技术基础Fundamentalsofgesturerecognitiontechnology手势识别是一种通过捕捉和分析用户的肢体动作，特别是手部和手指的动作，以实现人机交互的技术。它结合了图像处理、模式识别、机器学习等领域的知识，以实现对复杂手势的准确识别。在智能手势识别手套的设计中，我们主要依赖传感器技术和信号处理技术来实现手势的捕捉和识别。Gesturerecognitionisatechnologythatenableshuman-computerinteractionbycapturingandanalyzinguserbodymovements,especiallyhandandfingermovements.Itcombinesknowledgefromfieldssuchasimageprocessing,patternrecognition,andmachinelearningtoachieveaccuraterecognitionofcomplexgestures.Inthedesignofintelligentgesturerecognitiongloves,wemainlyrelyonsensortechnologyandsignalprocessingtechnologytoachievegesturecaptureandrecognition.传感器技术：在智能手套中，常用的传感器有加速度计、陀螺仪、弯曲传感器和电容式触摸传感器等。加速度计和陀螺仪可以捕捉手部和手指的动态运动，如挥动、旋转等。弯曲传感器则能够感知手指的弯曲程度，从而判断出手势的类型。电容式触摸传感器则可以用于检测手套上的触摸动作，进一步丰富手势的种类。Sensortechnology:Insmartgloves,commonlyusedsensorsincludeaccelerometers,gyroscopes,bendingsensors,andcapacitivetouchsensors.Accelerometersandgyroscopescancapturethedynamicmovementsofhandsandfingers,suchasswinging,rotating,etc.Thebendingsensorcansensethedegreeoffingerbendinganddeterminethetypeofgesture.Capacitivetouchsensorscanbeusedtodetecttouchmovementsongloves,furtherenrichingthevarietyofgestures.信号处理技术：捕捉到的传感器数据需要经过一定的处理才能被识别为特定的手势。这包括数据的预处理、特征提取和分类识别等步骤。预处理主要是对原始数据进行去噪、滤波等操作，以提高数据的质量。特征提取则是从预处理后的数据中提取出对手势识别有用的信息，如手势的动态特征、静态特征等。分类识别则是利用机器学习算法，如支持向量机（SVM）、神经网络等，对提取出的特征进行分类，从而实现手势的识别。Signalprocessingtechnology:Thecapturedsensordataneedstoundergocertainprocessingtoberecognizedasspecificgestures.Thisincludesstepssuchasdatapreprocessing,featureextraction,andclassificationrecognition.Preprocessingmainlyinvolvesdenoising,filtering,andotheroperationsontheoriginaldatatoimprovethequalityofthedata.Featureextractionistheprocessofextractingusefulinformationforgesturerecognitionfrompreprocesseddata,suchasdynamicandstaticfeaturesofgestures.Classificationrecognitionistheuseofmachinelearningalgorithms,suchassupportvectormachines(SVM),neuralnetworks,etc.,toclassifytheextractedfeaturesandachievegesturerecognition.手势识别算法：手势识别算法是实现手势识别的核心。它需要根据手套上传感器捕捉到的数据，结合信号处理技术，对手势进行准确的识别和分类。目前常用的手势识别算法有基于规则的方法、模板匹配方法和机器学习方法等。基于规则的方法是根据一定的规则或逻辑来判断手势的类型，适用于简单的手势识别。模板匹配方法则是将捕捉到的手势数据与预先定义的手势模板进行匹配，从而识别出手势。机器学习方法则是通过训练大量的手势数据来建立手势识别的模型，具有更高的识别精度和更强的泛化能力。Gesturerecognitionalgorithm:Gesturerecognitionalgorithmisthecoreofimplementinggesturerecognition.Itneedstoaccuratelyrecognizeandclassifygesturesbasedonthedatacapturedbysensorsonthegloves,combinedwithsignalprocessingtechnology.Thecommonlyusedgesturerecognitionalgorithmscurrentlyincluderule-basedmethods,templatematchingmethods,andmachinelearningmethods.Therule-basedapproachistodeterminethetypeofgesturebasedoncertainrulesorlogic,andissuitableforsimplegesturerecognition.Thetemplatematchingruleistomatchthecapturedgesturedatawithapre-definedgesturetemplate,inordertorecognizethegesture.Machinelearningmethodsestablishgesturerecognitionmodelsbytrainingalargeamountofgesturedata,whichhavehigherrecognitionaccuracyandstrongergeneralizationability.在STM32单片机的智能手势识别手套的设计中，我们需要合理选择传感器类型和数量，设计有效的信号处理算法，以及选择合适的手势识别算法，以实现准确、快速和稳定的手势识别。我们还需要考虑系统的功耗和实时性等因素，以满足实际应用的需求。InthedesignofintelligentgesturerecognitionglovesforSTM32microcontroller,weneedtochoosetheappropriatetypeandquantityofsensors,designeffectivesignalprocessingalgorithms,andselectappropriategesturerecognitionalgorithmstoachieveaccurate,fast,andstablegesturerecognition.Wealsoneedtoconsiderfactorssuchaspowerconsumptionandreal-timeperformanceofthesystemtomeettheneedsofpracticalapplications.三、STM32单片机概述OverviewofSTM32microcontrollerSTM32单片机，全称意为STMicroelectronics32-bitFlashMicrocontroller，是由全球知名的半导体制造商意法半导体（STMicroelectronics）推出的一款32位Flash微控制器。自2004年面世以来，STM32单片机凭借其高性能、低功耗、易于编程和丰富的外设资源等优点，在嵌入式系统领域获得了广泛的应用。STM32microcontroller,alsoknownasSTMicroelectronics32-bitFlashMicrocontroller,isa32-bitFlashmicrocontrollerlaunchedbySTMicroelectronics,agloballyrenownedsemiconductormanufacturer.Sinceitslaunchin2004,theSTM32microcontrollerhasbeenwidelyusedinthefieldofembeddedsystemsduetoitsadvantagesofhighperformance,lowpowerconsumption,easyprogramming,andabundantperipheralresources.STM32单片机采用ARMCortex-M系列核心，具备高集成度、高性能和低功耗的特点。该系列单片机内置了高速存储器、多种通信接口和丰富的外设模块，如GPIO、UART、SPI、I2C、ADC、DAC、PWM等，能够满足各种复杂应用的需求。STM32单片机还支持多种编程语言，如C、C++和汇编语言等，方便开发者进行编程和调试。TheSTM32microcontrolleradoptstheARMCortex-Mseriescore,whichhasthecharacteristicsofhighintegration,highperformance,andlowpowerconsumption.Thisseriesofmicrocontrollersisequippedwithhigh-speedmemory,variouscommunicationinterfaces,andrichperipheralmodulessuchasGPIO,UART,SPI,I2C,ADC,DAC,PWM,etc.,whichcanmeettheneedsofvariouscomplexapplications.TheSTM32microcontrolleralsosupportsmultipleprogramminglanguages,suchasC,C++,andassemblylanguage,makingitconvenientfordeveloperstoprogramanddebug.在智能手势识别手套的设计中，STM32单片机扮演着至关重要的角色。作为整个系统的核心控制器，STM32单片机负责处理传感器采集的手势数据、实现手势识别算法、控制执行机构的动作以及与其他设备的通信等任务。通过STM32单片机的强大功能，可以实现手套对手势的准确识别、快速响应和高效控制，为手势识别技术的发展提供了有力的支持。Inthedesignofintelligentgesturerecognitiongloves,theSTM32microcontrollerplaysacrucialrole.Asthecorecontrolleroftheentiresystem,theSTM32microcontrollerisresponsibleforprocessinggesturedatacollectedbysensors,implementinggesturerecognitionalgorithms,controllingtheactionsofexecutingmechanisms,andcommunicatingwithotherdevices.ThroughthepowerfulfunctionsoftheSTM32microcontroller,accuraterecognition,rapidresponse,andefficientcontrolofglovegesturescanbeachieved,providingstrongsupportforthedevelopmentofgesturerecognitiontechnology.STM32单片机以其卓越的性能和丰富的功能，为智能手势识别手套的设计与应用提供了坚实的基础。在未来的发展中，随着手势识别技术的不断进步和应用领域的拓展，STM32单片机将继续发挥重要作用，推动智能手势识别手套技术的创新与发展。TheSTM32microcontrollerprovidesasolidfoundationforthedesignandapplicationofintelligentgesturerecognitiongloveswithitsexcellentperformanceandrichfunctions.Inthefuturedevelopment,withthecontinuousprogressofgesturerecognitiontechnologyandtheexpansionofapplicationfields,theSTM32microcontrollerwillcontinuetoplayanimportantroleinpromotingtheinnovationanddevelopmentofintelligentgesturerecognitionglovetechnology.四、智能手势识别手套的设计DesignofIntelligentGestureRecognitionGloves在设计基于STM32单片机的智能手势识别手套时，我们主要考虑了硬件设计、软件设计以及用户界面的友好性。以下是详细的设计步骤和考虑因素。WhendesigningintelligentgesturerecognitionglovesbasedonSTM32microcontroller,wemainlyconsideredhardwaredesign,softwaredesign,anduser-friendlyinterface.Thefollowingaredetaileddesignstepsandconsiderations.手套的硬件设计主要围绕STM32单片机进行。我们需要选择适合的STM32型号，考虑到手势识别的复杂性和实时性要求，我们选择了性能较高的STM32F4系列。还需要设计手套的传感器阵列，这包括用于检测手指弯曲的柔性电阻传感器和用于定位手部位置的九轴传感器（包括三轴加速度计和三轴陀螺仪，以及三轴磁力计）。这些传感器与STM32单片机通过I2C或SPI等接口进行通信，将采集到的数据传输到单片机进行处理。ThehardwaredesignofglovesmainlyrevolvesaroundtheSTM32microcontroller.WeneedtochooseasuitableSTM32model,andconsideringthecomplexityandreal-timerequirementsofgesturerecognition,wehavechosentheSTM32F4serieswithhigherperformance.Wealsoneedtodesignasensorarrayforgloves,whichincludesflexibleresistancesensorsfordetectingfingerbendingandnineaxissensorsforlocatinghandpositions(includingthree-axisaccelerometersandgyroscopes,aswellasthree-axismagnetometers).ThesesensorscommunicatewiththeSTM32microcontrollerthroughinterfacessuchasI2CorSPI,andtransmitthecollecteddatatothemicrocontrollerforprocessing.软件设计主要包括数据采集、预处理、手势识别以及命令输出四个部分。我们需要编写驱动程序，使STM32单片机能够正确读取传感器数据。然后，对采集到的原始数据进行预处理，如滤波、去噪等，以提高数据的质量。接下来，利用机器学习算法（如支持向量机、神经网络等）对预处理后的数据进行手势识别。根据识别的手势生成相应的控制命令，通过蓝牙或其他无线通信技术发送给目标设备。Thesoftwaredesignmainlyincludesfourparts:dataacquisition,preprocessing,gesturerecognition,andcommandoutput.WeneedtowritedriverprogramstoenabletheSTM32microcontrollertocorrectlyreadsensordata.Then,preprocessthecollectedrawdata,suchasfilteringanddenoising,toimprovethequalityofthedata.Next,usemachinelearningalgorithmssuchassupportvectormachines,neuralnetworks,etc.toperformgesturerecognitiononpreprocesseddata.GeneratecorrespondingcontrolcommandsbasedonrecognizedgesturesandsendthemtothetargetdevicethroughBluetoothorotherwirelesscommunicationtechnologies.为了让用户能够直观地了解和使用手套，我们设计了一个友好的用户界面。该界面能够实时显示手套的状态（如电量、连接状态等），并允许用户对手套进行配置（如选择识别模式、调整识别阈值等）。界面还提供了手势教程和手势识别结果的反馈，帮助用户更好地理解和使用手套。Inordertoenableuserstointuitivelyunderstandandusegloves,wehavedesignedauser-friendlyinterface.Thisinterfacecandisplaythestatusofglovesinreal-time(suchasbatterylevel,connectionstatus,etc.)andallowuserstoconfiguregloves(suchasselectingrecognitionmode,adjustingrecognitionthreshold,etc.).Theinterfacealsoprovidesgesturetutorialsandfeedbackongesturerecognitionresults,helpingusersbetterunderstandandusegloves.在完成硬件和软件设计后，我们进行了系统集成和测试。我们对各个模块进行了单独测试，确保其功能正常。然后，将各个模块集成到手套中，进行整体测试。在测试过程中，我们模拟了多种实际使用场景，对手套的识别准确率、响应速度等性能进行了评估。根据测试结果，我们对设计进行了优化和改进，以提高手套的性能和用户体验。Aftercompletingthehardwareandsoftwaredesign,weconductedsystemintegrationandtesting.Weconductedseparatetestsoneachmoduletoensureitsproperfunctionality.Then,integrateeachmoduleintothegloveforoveralltesting.Duringthetestingprocess,wesimulatedvariouspracticalusagescenariosandevaluatedtherecognitionaccuracy,responsespeed,andotherperformanceofthegloves.Basedonthetestresults,wehaveoptimizedandimprovedthedesigntoenhancetheperformanceanduserexperienceofthegloves.基于STM32单片机的智能手势识别手套的设计涉及硬件、软件以及用户界面等多个方面。通过合理的设计和优化，我们可以实现一个功能强大、性能稳定、易于使用的智能手势识别手套，为人机交互领域的发展做出贡献。ThedesignofintelligentgesturerecognitionglovesbasedonSTM32microcontrollerinvolvesmultipleaspectssuchashardware,software,anduserinterface.Throughreasonabledesignandoptimization,wecanachieveapowerful,stable,andeasy-to-useintelligentgesturerecognitionglove,contributingtothedevelopmentofhuman-computerinteraction.五、手势识别算法的研究与实现ResearchandImplementationofGestureRecognitionAlgorithms手势识别是智能手势识别手套的核心功能，其实现依赖于高效且精确的手势识别算法。在本设计中，我们针对STM32单片机平台，研究并实现了一套基于机器学习和传感器数据融合的手势识别算法。Gesturerecognitionisthecorefunctionofintelligentgesturerecognitiongloves,anditsimplementationreliesonefficientandaccurategesturerecognitionalgorithms.Inthisdesign,wehavestudiedandimplementedagesturerecognitionalgorithmbasedonmachinelearningandsensordatafusionfortheSTM32microcontrollerplatform.我们对常见的手势进行了分类和特征提取。通过对手势的动态和静态特征进行深入分析，我们选择了适合STM32单片机处理能力的特征集，包括手指的弯曲程度、手掌的倾斜角度、手腕的旋转方向等。这些特征的选择，既保证了手势识别的准确性，又保证了算法的实时性。Wehaveclassifiedandextractedfeaturesfromcommongestures.Throughin-depthanalysisofthedynamicandstaticcharacteristicsofgestures,wehaveselectedafeaturesetthatissuitablefortheprocessingcapabilitiesoftheSTM32microcontroller,includingfingerbendingdegree,palmtiltangle,wristrotationdirection,etc.Theselectionofthesefeaturesensuresboththeaccuracyofgesturerecognitionandthereal-timeperformanceofthealgorithm.接下来，我们设计了一种基于支持向量机（SVM）的手势分类器。SVM是一种高效的监督学习算法，特别适用于小样本、高维度的分类问题。我们通过采集大量的手势样本，对SVM分类器进行训练和优化，使其能够准确地区分不同的手势。Next,wedesignedagestureclassifierbasedonSupportVectorMachine(SVM).SVMisanefficientsupervisedlearningalgorithm,particularlysuitableforsmallsample,high-dimensionalclassificationproblems.WetrainandoptimizetheSVMclassifierbycollectingalargenumberofgesturesamplestoaccuratelydistinguishdifferentgestures.在算法实现过程中，我们还采用了传感器数据融合技术，以提高手势识别的鲁棒性。具体来说，我们将来自手套上的多个传感器（如弯曲传感器、加速度计等）的数据进行融合，通过加权平均、卡尔曼滤波等方法，消除传感器之间的噪声和干扰，从而得到更加准确和稳定的手势数据。Inthealgorithmimplementationprocess,wealsoadoptedsensordatafusiontechnologytoimprovetherobustnessofgesturerecognition.Specifically,wefusedatafrommultiplesensorsongloves,suchasbendingsensorsandaccelerometers,andeliminatenoiseandinterferencebetweensensorsthroughmethodssuchasweightedaveragingandKalmanfiltering,inordertoobtainmoreaccurateandstablegesturedata.我们将手势识别算法与STM32单片机的硬件平台相结合，实现了手势识别手套的实时控制。通过编写针对STM32单片机的手势识别软件，我们将手势数据转换为控制指令，驱动手套上的执行器（如电机、LED等）进行相应的动作。我们还设计了一种友好的人机交互界面，使用户可以通过手势来操作和控制智能设备，从而提高了用户体验和便捷性。WecombinedthegesturerecognitionalgorithmwiththehardwareplatformoftheSTM32microcontrollertoachievereal-timecontrolofgesturerecognitiongloves.BywritinggesturerecognitionsoftwarefortheSTM32microcontroller,weconvertgesturedataintocontrolcommandsanddrivetheactuatorsonthegloves(suchasmotors,LEDs,etc.)toperformcorrespondingactions.Wehavealsodesignedauser-friendlyhuman-computerinteractioninterfacethatallowsuserstooperateandcontrolsmartdevicesthroughgestures,therebyimprovinguserexperienceandconvenience.我们通过研究并实现了一套基于STM32单片机的手势识别算法，成功地将手势识别技术应用于智能手套中。该算法具有高效、准确、实时的特点，为智能手势识别手套的设计与应用提供了有力的支持。WehavesuccessfullyappliedgesturerecognitiontechnologytosmartglovesbystudyingandimplementingagesturerecognitionalgorithmbasedontheSTM32microcontroller.Thisalgorithmhasthecharacteristicsofhighefficiency,accuracy,andreal-time,providingstrongsupportforthedesignandapplicationofintelligentgesturerecognitiongloves.六、智能手势识别手套的应用Theapplicationofintelligentgesturerecognitiongloves随着科技的飞速发展，人机交互技术在日常生活中扮演着越来越重要的角色。基于STM32单片机的智能手势识别手套，作为一种先进的人机交互设备，其应用前景十分广阔。Withtherapiddevelopmentoftechnology,human-computerinteractiontechnologyisplayinganincreasinglyimportantroleindailylife.TheintelligentgesturerecognitionglovebasedonSTM32microcontroller,asanadvancedhuman-computerinteractiondevice,hasaverybroadapplicationprospect.在医疗领域，智能手势识别手套可以帮助医生进行更为精准和高效的手术操作。医生可以通过手势直接控制手术器械，减少操作过程中的误差，提高手术成功率。手套还可以监测医生的手部运动和力度，为医生提供实时的反馈，帮助他们调整手术策略。Inthemedicalfield,intelligentgesturerecognitionglovescanhelpdoctorsperformmorepreciseandefficientsurgicaloperations.Doctorscandirectlycontrolsurgicalinstrumentsthroughgestures,reducingerrorsduringtheoperationprocessandimprovingthesuccessrateofsurgery.Glovescanalsomonitorthedoctor'shandmovementsandstrength,providingreal-timefeedbacktodoctorsandhelpingthemadjustsurgicalstrategies.在娱乐和游戏行业，智能手势识别手套为用户带来了全新的交互体验。用户可以通过简单的手势操作来控制游戏角色或进行音乐演奏，使得娱乐活动更加直观和有趣。手套还可以结合虚拟现实技术，为用户提供沉浸式的游戏体验。Intheentertainmentandgamingindustries,intelligentgesturerecognitionglovesbringusersabrandnewinteractiveexperience.Userscancontrolgamecharactersorperformmusicthroughsimplegestureoperations,makingentertainmentactivitiesmoreintuitiveandinteresting.Glovescanalsobecombinedwithvirtualrealitytechnologytoprovideuserswithanimmersivegamingexperience.在工业生产中，智能手势识别手套可以大大提高工人的工作效率和安全性。工人可以通过手势来操作机器或控制生产流程，减少传统操作方式中的繁琐和危险。同时，手套还可以实时监测工人的手部状态和安全风险，为工人提供及时的预警和保护。Inindustrialproduction,intelligentgesturerecognitionglovescangreatlyimprovetheworkefficiencyandsafetyofworkers.Workerscanoperatemachinesorcontrolproductionprocessesthroughgestures,reducingthecomplexityanddangeroftraditionaloperatingmethods.Atthesametime,glovescanalsomonitorthehandconditionandsafetyrisksofworkersinrealtime,providingtimelywarningandprotectionforworkers.在教育领域，智能手势识别手套可以作为一种创新的教学工具。教师可以通过手套进行直观的手势教学，帮助学生更好地理解和掌握知识。学生也可以利用手套进行实践操作，提高他们的动手能力和创造力。Inthefieldofeducation,intelligentgesturerecognitionglovescanserveasaninnovativeteachingtool.Teacherscanuseglovesforintuitivegestureteaching,helpingstudentsbetterunderstandandmasterknowledge.Studentscanalsouseglovesforpracticaloperationstoimprovetheirhands-onabilityandcreativity.智能手势识别手套在智能家居、航空航天、军事等领域也有着广泛的应用。随着技术的不断进步和成本的降低，相信智能手势识别手套将在更多领域发挥重要作用，为人们的生活和工作带来更多便利和乐趣。Intelligentgesturerecognitionglovesarealsowidelyusedinsmarthomes,aerospace,militaryandotherfields.Withthecontinuousadvancementoftechnologyandthereductionofcosts,itisbelievedthatintelligentgesturerecognitiongloveswillplayanimportantroleinmorefields,bringingmoreconvenienceandfuntopeople'slivesandwork.七、结论与展望ConclusionandOutlook本研究设计并实现了一种基于STM32单片机的智能手势识别手套，通过集成弯曲传感器、加速度传感器以及无线通信模块，实现了对手部姿态的精准捕捉与实时传输。手套的设计充分考虑了人体工学与舒适性，确保用户在使用过程中能够保持自然的手部动作。同时，STM32单片机的强大处理能力与低功耗特性使得手套在保持高性能的同时，也具备了较长的续航能力。ThisstudydesignedandimplementedanintelligentgesturerecognitionglovebasedontheSTM32microcontroller.Byintegratingbendingsensors,accelerationsensors,andwirelesscommunicationmodules,precisecaptureandreal-timetransmissionofhandposturewereachieved.Thedesignofglovesfullyconsidersergonomicsandcomfort,ensuringthatuserscanmaintainnaturalhandmovementsduringuse.Atthesametime,thepowerfulprocessingpowerandlow