{"id":3459,"date":"2025-09-03T11:12:59","date_gmt":"2025-09-03T09:12:59","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/activity-recognition\/"},"modified":"2026-02-11T11:54:28","modified_gmt":"2026-02-11T10:54:28","slug":"human-activity-recognition-machine-learning","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/human-activity-recognition-machine-learning\/","title":{"rendered":"Human activity recognition machine learning with smartphone data"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3459\" class=\"elementor elementor-3459\" data-elementor-post-type=\"learning\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7cf4c62 e-grid e-con-full e-con e-parent\" data-id=\"7cf4c62\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-ef2c7c1 e-con-full e-flex e-con e-child\" data-id=\"ef2c7c1\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ec8745a elementor-widget elementor-widget-heading\" data-id=\"ec8745a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Introduction<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d2d8bab elementor-widget elementor-widget-text-editor\" data-id=\"d2d8bab\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Human <a href=\"https:\/\/www.neuraldesigner.com\/solutions\/activity-recognition\">activity recognition<\/a>\u00a0 (HAR) using machine learning can support healthcare applications by analyzing smartphone movement data to detect daily activities.<\/p><p>In this study, 30 volunteers (aged 19\u201348) performed six activities while a waist-worn smartphone recorded accelerometer and gyroscope data at 50 Hz, with video labeling for accuracy.<\/p><p>Healthcare professionals can test this approach with Neural Designer\u2019s trial version.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ad401b4 e-con-full e-flex e-con e-child\" data-id=\"ad401b4\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7ebfdc0 elementor-widget__width-initial boton_descarga elementor-widget elementor-widget-button\" data-id=\"7ebfdc0\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/www.neuraldesigner.com\/my-account\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-download\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M216 0h80c13.3 0 24 10.7 24 24v168h87.7c17.8 0 26.7 21.5 14.1 34.1L269.7 378.3c-7.5 7.5-19.8 7.5-27.3 0L90.1 226.1c-12.6-12.6-3.7-34.1 14.1-34.1H192V24c0-13.3 10.7-24 24-24zm296 376v112c0 13.3-10.7 24-24 24H24c-13.3 0-24-10.7-24-24V376c0-13.3 10.7-24 24-24h146.7l49 49c20.1 20.1 52.5 20.1 72.6 0l49-49H488c13.3 0 24 10.7 24 24zm-124 88c0-11-9-20-20-20s-20 9-20 20 9 20 20 20 20-9 20-20zm64 0c0-11-9-20-20-20s-20 9-20 20 9 20 20 20 20-9 20-20z\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Free Trial<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c17e872 elementor-widget elementor-widget-heading\" data-id=\"c17e872\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Contents<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ab690c2 elementor-widget elementor-widget-text-editor\" data-id=\"ab690c2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The following index outlines the steps for performing the analysis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-246c522 e-grid e-con-full e-con e-child\" data-id=\"246c522\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a018056 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"a018056\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#model_type\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">1.Model type<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9b91223 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"9b91223\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#dataset\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">2.Dataset<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3d34400 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"3d34400\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#neural_network\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">3.Neural network<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-11956f6 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"11956f6\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#training_strategy\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">4.Training strategy<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-223f87a elementor-align-center elementor-widget elementor-widget-button\" data-id=\"223f87a\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#testing_analysis\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">5.Testing analysis<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4459b90 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"4459b90\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#model_deployment\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">6.Model deployment<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-193eb5f elementor-widget elementor-widget-heading\" data-id=\"193eb5f\" data-element_type=\"widget\" id=\"model_type\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">1. Model type<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a794713 elementor-widget elementor-widget-text-editor\" data-id=\"a794713\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul><li data-start=\"60\" data-end=\"179\"><strong data-start=\"60\" data-end=\"77\">Problem type:<\/strong> Multiclass <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications#Classification\">classification<\/a> (walking, walking_upstairs, walking_downstairs, sitting, standing, lying)<\/li><li data-start=\"60\" data-end=\"179\"><strong data-start=\"181\" data-end=\"190\">Goal:<\/strong> Model the probability of each activity based on input variables to support healthcare applications such as patient monitoring, rehabilitation, and lifestyle management.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5b5f089 elementor-widget elementor-widget-heading\" data-id=\"5b5f089\" data-element_type=\"widget\" id=\"dataset\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">2. Data set<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-505b728 elementor-widget elementor-widget-heading\" data-id=\"505b728\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Data source<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-35de936 elementor-widget elementor-widget-text-editor\" data-id=\"35de936\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The file activity_recognition.csv contains 10299 samples, each of them with 561 inputs and one categorical target.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ed7b813 e-con-full e-flex e-con e-child\" data-id=\"ed7b813\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-27e24c5 elementor-widget__width-initial boton_descarga elementor-widget elementor-widget-button\" data-id=\"27e24c5\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/activityrecognition.zip\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-file-download\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224 136V0H24C10.7 0 0 10.7 0 24v464c0 13.3 10.7 24 24 24h336c13.3 0 24-10.7 24-24V160H248c-13.2 0-24-10.8-24-24zm76.45 211.36l-96.42 95.7c-6.65 6.61-17.39 6.61-24.04 0l-96.42-95.7C73.42 337.29 80.54 320 94.82 320H160v-80c0-8.84 7.16-16 16-16h32c8.84 0 16 7.16 16 16v80h65.18c14.28 0 21.4 17.29 11.27 27.36zM377 105L279.1 7c-4.5-4.5-10.6-7-17-7H256v128h128v-6.1c0-6.3-2.5-12.4-7-16.9z\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Dataset<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c2ff246 elementor-widget elementor-widget-heading\" data-id=\"c2ff246\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Variables<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-11b5d7f elementor-widget elementor-widget-text-editor\" data-id=\"11b5d7f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The following list summarizes the variables&#8217; information:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0a85e05 elementor-widget elementor-widget-heading\" data-id=\"0a85e05\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Sensor signals \u2013 Time and frequency domain<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-971ef97 elementor-widget elementor-widget-text-editor\" data-id=\"971ef97\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul data-start=\"424\" data-end=\"1264\"><li data-start=\"424\" data-end=\"507\"><p data-start=\"426\" data-end=\"507\"><strong data-start=\"426\" data-end=\"457\">body_acceleration (x, y, z)<\/strong> \u2013 Linear acceleration of the body along 3 axes.<\/p><\/li><li data-start=\"508\" data-end=\"596\"><p data-start=\"510\" data-end=\"596\"><strong data-start=\"510\" data-end=\"544\">gravity_acceleration (x, y, z)<\/strong> \u2013 Gravity component of acceleration along 3 axes.<\/p><\/li><li data-start=\"597\" data-end=\"676\"><p data-start=\"599\" data-end=\"676\"><strong data-start=\"599\" data-end=\"635\">body_acceleration_jerk (x, y, z)<\/strong> \u2013 Rate of change of body acceleration.<\/p><\/li><li data-start=\"677\" data-end=\"773\"><p data-start=\"679\" data-end=\"773\"><strong data-start=\"679\" data-end=\"711\">body_angular_speed (x, y, z)<\/strong> \u2013 Angular velocity of the body from gyroscope measurements.<\/p><\/li><li data-start=\"774\" data-end=\"855\"><p data-start=\"776\" data-end=\"855\"><strong data-start=\"776\" data-end=\"815\">body_angular_acceleration (x, y, z)<\/strong> \u2013 Rate of change of angular velocity.<\/p><\/li><li data-start=\"856\" data-end=\"932\"><p data-start=\"858\" data-end=\"932\"><strong data-start=\"858\" data-end=\"889\">body_acceleration_magnitude<\/strong> \u2013 Magnitude of body acceleration vector.<\/p><\/li><li data-start=\"933\" data-end=\"1015\"><p data-start=\"935\" data-end=\"1015\"><strong data-start=\"935\" data-end=\"969\">gravity_acceleration_magnitude<\/strong> \u2013 Magnitude of gravity acceleration vector.<\/p><\/li><li data-start=\"1016\" data-end=\"1102\"><p data-start=\"1018\" data-end=\"1102\"><strong data-start=\"1018\" data-end=\"1054\">body_acceleration_jerk_magnitude<\/strong> \u2013 Magnitude of body acceleration jerk vector.<\/p><\/li><li data-start=\"1103\" data-end=\"1176\"><p data-start=\"1105\" data-end=\"1176\"><strong data-start=\"1105\" data-end=\"1137\">body_angular_speed_magnitude<\/strong> \u2013 Magnitude of angular speed vector.<\/p><\/li><li data-start=\"1177\" data-end=\"1264\"><p data-start=\"1179\" data-end=\"1264\"><strong data-start=\"1179\" data-end=\"1218\">body_angular_acceleration_magnitude<\/strong> \u2013 Magnitude of angular acceleration vector.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8314c60 elementor-widget elementor-widget-heading\" data-id=\"8314c60\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Target variable<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-091191b elementor-widget elementor-widget-text-editor\" data-id=\"091191b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul data-start=\"1288\" data-end=\"1475\"><li data-start=\"1288\" data-end=\"1475\"><p data-start=\"1290\" data-end=\"1365\"><strong data-start=\"1290\" data-end=\"1308\">activity<\/strong>\u00a0\u2013 The human activity performed during data collection: Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing, and Lying.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a64a052 elementor-widget elementor-widget-heading\" data-id=\"a64a052\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Variables distributions<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-30ac652 elementor-widget elementor-widget-text-editor\" data-id=\"30ac652\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Variable <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Distributions\">distributions<\/a> can be calculated; the figure shows the number of samples for each activity in the dataset.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b63fd55 elementor-widget elementor-widget-image\" data-id=\"b63fd55\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"657\" height=\"380\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/label-distribution-pie-chart.png\" class=\"attachment-large size-large wp-image-19706\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/label-distribution-pie-chart.png 657w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/label-distribution-pie-chart-300x174.png 300w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2025\/09\/label-distribution-pie-chart-600x347.png 600w\" sizes=\"(max-width: 657px) 100vw, 657px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5007281 elementor-widget elementor-widget-text-editor\" data-id=\"5007281\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>As we can see, the number of instances belonging to each category is similar. Therefore, this data set exhibits good balance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0283638 elementor-widget elementor-widget-heading\" data-id=\"0283638\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Input-target correlations<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9281371 elementor-widget elementor-widget-text-editor\" data-id=\"9281371\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#InputsTargetsCorrelations\">input-target correlations<\/a> indicate which sensor signals most influence the classification of activities and, therefore, are more relevant to our analysis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3bad722 elementor-widget elementor-widget-image\" data-id=\"3bad722\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"600\" height=\"810\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/2.activity-recognition-inputs-targets-correlations.webp\" class=\"attachment-large size-large wp-image-16680\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/2.activity-recognition-inputs-targets-correlations.webp 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/2.activity-recognition-inputs-targets-correlations-222x300.webp 222w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e249f17 elementor-widget elementor-widget-text-editor\" data-id=\"e249f17\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The most correlated variables with activity classification are <strong>tBodyAccJerk-mean-Z<\/strong>, <strong>fBodyAcc-kurtosis-X<\/strong>, and <strong>fBodyAcc-max-X<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3bc66a4 elementor-widget elementor-widget-heading\" data-id=\"3bc66a4\" data-element_type=\"widget\" id=\"neural_network\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">3. Neural network<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-88c122b elementor-widget elementor-widget-text-editor\" data-id=\"88c122b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>A neural network is an artificial intelligence model inspired by how the human brain processes information.<\/p><p>It is organized in layers: the input layer receives the variables, the hidden layers combine them to detect relevant patterns, and the output layer provides the probability of belonging to a given class.<\/p><p>Trained with historical data, the network learns to recognize patterns and distinguish between categories, offering objective support for decision-making.<\/p><p>The network processes the smartphone sensor signals, combines them in hidden layers, and outputs the probability for each activity class.<\/p><p>The connections illustrate how the different sensor variables contribute to the classification.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d23e589 elementor-widget elementor-widget-heading\" data-id=\"d23e589\" data-element_type=\"widget\" id=\"training_strategy\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">4. Training strategy<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f384676 elementor-widget elementor-widget-text-editor\" data-id=\"f384676\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Training a neural network uses a loss function to measure errors and an optimization algorithm to adjust the model, ensuring it learns from data while avoiding overfitting for good performance on new cases.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8ae3515 elementor-widget elementor-widget-image\" data-id=\"8ae3515\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"600\" height=\"400\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/3.activity-recognition-training-history.webp\" class=\"attachment-large size-large wp-image-16679\" alt=\"\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/3.activity-recognition-training-history.webp 600w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/3.activity-recognition-training-history-300x200.webp 300w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-75205f6 elementor-widget elementor-widget-text-editor\" data-id=\"75205f6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The model was trained for accuracy and stability, with training and selection errors decreasing steadily (0.008 and 0.048 NSE), indicating effective learning and generalization to new activity instances.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d6d40e0 elementor-widget elementor-widget-heading\" data-id=\"d6d40e0\" data-element_type=\"widget\" id=\"testing_analysis\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">5. Testing analysis<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-33f7e71 elementor-widget elementor-widget-text-editor\" data-id=\"33f7e71\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Once the model is trained, we perform a <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\">testing\u00a0<\/a><span style=\"margin: 0px; padding: 0px;\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\" target=\"_blank\" rel=\"noopener\">analysis\u00a0<\/a>to<\/span>\u00a0validate its prediction capacity.<\/p><p>In particular, we use a subset of data that has not been used before, the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#TestingInstances\">testing instances<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c3ac32f elementor-widget elementor-widget-heading\" data-id=\"c3ac32f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Confusion matrix<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d092ffe elementor-widget elementor-widget-text-editor\" data-id=\"d092ffe\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis#ConfusionMatrix\">confusion matrix<\/a> shows the model\u2019s performance by comparing predicted and actual activities. It includes:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b475a3b elementor-widget elementor-widget-text-editor\" data-id=\"b475a3b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul><li data-start=\"195\" data-end=\"250\"><p data-start=\"197\" data-end=\"250\"><strong data-start=\"197\" data-end=\"216\">True positives:<\/strong> activities correctly identified<\/p><\/li><li data-start=\"251\" data-end=\"326\"><p data-start=\"253\" data-end=\"326\"><strong data-start=\"253\" data-end=\"273\">False positives:<\/strong> activities incorrectly identified as another class<\/p><\/li><li data-start=\"327\" data-end=\"396\"><p data-start=\"329\" data-end=\"396\"><strong data-start=\"329\" data-end=\"349\">False negatives:<\/strong> activities that were missed or misclassified<\/p><\/li><li data-start=\"397\" data-end=\"489\"><p data-start=\"399\" data-end=\"489\"><strong data-start=\"399\" data-end=\"418\">True negatives:<\/strong> activities correctly recognized as not belonging to a given class<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d4d3fd4 e-con-full e-flex e-con e-child\" data-id=\"d4d3fd4\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d439254 elementor-widget elementor-widget-text-editor\" data-id=\"d439254\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<table style=\"display: block;\"><tbody><tr><th>\u00a0<\/th><th>Predicted STANDING<\/th><th>Predicted SITTING<\/th><th>Predicted LAYING<\/th><th>Predicted WALKING<\/th><th>Predicted WALKING DOWNSTAIRS<\/th><th>Predicted WALKING UPSTAIRS<\/th><\/tr><tr><th style=\"text-align: left;\">Real STANDING<\/th><td style=\"text-align: right;\">376\u00a0<\/td><td style=\"text-align: right;\">18<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><\/tr><tr><th style=\"text-align: left;\">Real SITTING<\/th><td style=\"text-align: right;\">18\u00a0<\/td><td style=\"text-align: right;\">330<\/td><td style=\"text-align: right;\">1<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><\/tr><tr><th style=\"text-align: left;\">Real LAYING<\/th><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">2<\/td><td style=\"text-align: right;\">402<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><\/tr><tr><th style=\"text-align: left;\">Real WALKING<\/th><td style=\"text-align: right;\">1<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">307<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><\/tr><tr><th style=\"text-align: left;\">Real WALKING DOWNSTAIRS<\/th><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">279<\/td><td style=\"text-align: right;\">1<\/td><\/tr><tr><th style=\"text-align: left;\">Real WALKING UPSTAIRS<\/th><td style=\"text-align: right;\">1<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">0<\/td><td style=\"text-align: right;\">5<\/td><td style=\"text-align: right;\">318<\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9cafd44 elementor-widget elementor-widget-text-editor\" data-id=\"9cafd44\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>In this example, <strong>97.72%<\/strong> of cases were <strong>correctly classified<\/strong>\u00a0and\u00a0<strong>2.28%<\/strong>\u00a0were\u00a0<strong>misclassified<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-60c8661 elementor-widget elementor-widget-heading\" data-id=\"60c8661\" data-element_type=\"widget\" id=\"model_deployment\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">6. Model deployment<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f6455cb elementor-widget elementor-widget-text-editor\" data-id=\"f6455cb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"121\" data-end=\"244\">Once validated, the neural network can be saved for deployment, allowing predictions of human activities from new accelerometer and gyroscope data.<\/p><p data-start=\"121\" data-end=\"244\">In deployment mode, researchers can use it as a real-time or offline recognition tool, with Neural Designer automatically exporting the model for easy integration into laboratory or application workflows.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7da11eb e-grid e-con-full e-con e-child\" data-id=\"7da11eb\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-50a99db e-con-full e-flex e-con e-child\" data-id=\"50a99db\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-11ba84c elementor-widget__width-initial boton_descarga elementor-widget elementor-widget-button\" data-id=\"11ba84c\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/www.neuraldesigner.com\/my-account\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-download\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M216 0h80c13.3 0 24 10.7 24 24v168h87.7c17.8 0 26.7 21.5 14.1 34.1L269.7 378.3c-7.5 7.5-19.8 7.5-27.3 0L90.1 226.1c-12.6-12.6-3.7-34.1 14.1-34.1H192V24c0-13.3 10.7-24 24-24zm296 376v112c0 13.3-10.7 24-24 24H24c-13.3 0-24-10.7-24-24V376c0-13.3 10.7-24 24-24h146.7l49 49c20.1 20.1 52.5 20.1 72.6 0l49-49H488c13.3 0 24 10.7 24 24zm-124 88c0-11-9-20-20-20s-20 9-20 20 9 20 20 20 20-9 20-20zm64 0c0-11-9-20-20-20s-20 9-20 20 9 20 20 20 20-9 20-20z\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Free Trial<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-96e6c32 elementor-widget elementor-widget-heading\" data-id=\"96e6c32\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusions<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-67c1fd4 elementor-widget elementor-widget-text-editor\" data-id=\"67c1fd4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"224\" data-end=\"454\">The human activity recognition model achieved excellent performance, correctly classifying 2012 of 2059 instances (2.3% misclassification).<\/p><p data-start=\"224\" data-end=\"454\">Key features\u2014such as Z-axis body acceleration jerk, frequency-domain acceleration, and maximum X-axis acceleration\u2014align with biomechanical principles.<\/p><p data-start=\"224\" data-end=\"454\">With strong generalization, this neural network can support healthcare professionals in monitoring activity, developing health apps, and providing personalized lifestyle and rehabilitation recommendations.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d69dba6 elementor-widget elementor-widget-heading\" data-id=\"d69dba6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">References<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-da5dcde elementor-widget elementor-widget-text-editor\" data-id=\"da5dcde\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul><li>UCI Machine Learning Repository\u00a0<a href=\"https:\/\/archive.ics.uci.edu\/ml\/datasets\/Human+Activity+Recognition+Using+Smartphones\" target=\"_blank\" rel=\"noopener\">Human Activity Recognition Using Smartphones Data Set<\/a>.<\/li><li>Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21st European Symposium on Artificial Neural Networks, Computational Intelligence, and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b13e200 elementor-widget elementor-widget-heading\" data-id=\"b13e200\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Related posts<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"author":13,"featured_media":2709,"template":"","categories":[29],"tags":[38],"class_list":["post-3459","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-examples","tag-healthcare"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Human activity recognition machine learning with smartphone data<\/title>\n<meta name=\"description\" content=\"Build a machine learning model to identify human activity recognition from 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