{"id":2879,"date":"2024-02-18T15:29:57","date_gmt":"2024-02-18T15:29:57","guid":{"rendered":"https:\/\/esisoc.com\/resource\/scaling-up-a-fitness-app-with-deep-neural-networks\/"},"modified":"2024-02-18T15:29:57","modified_gmt":"2024-02-18T15:29:57","slug":"scaling-up-a-fitness-app-with-deep-neural-networks","status":"publish","type":"resource","link":"https:\/\/esisoc.com\/fr\/resource\/developper-une-application-de-fitness-a-laide-de-reseaux-neuronaux-profonds\/","title":{"rendered":"Mise \u00e0 l'\u00e9chelle d'une application de fitness avec des r\u00e9seaux neuronaux profonds"},"content":{"rendered":"<h2 style=\"text-align: center;\">D\u00e9tails cl\u00e9s<\/h2>\n<p>Am\u00e9lioration des performances de l'application par 19%.<\/p>\n<div>\n<ul>\n<li>\n<div>D\u00e9fi<\/div>\n<div>D\u00e9veloppement de r\u00e9seaux neuronaux \u00e0 la demande<\/div>\n<\/li>\n<li>\n<div>Solution<\/div>\n<div>CNN pour la reconnaissance d'activit\u00e9s et la d\u00e9tection d'erreurs<\/div>\n<\/li>\n<li>\n<div>Technologies et outils<\/div>\n<div>PyTorch, Keras, Tensorflow, CoreML, OpenCV, ONNX<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<h2 style=\"text-align: center;\">Client<\/h2>\n<p>Le client est une startup sp\u00e9cialis\u00e9e dans la technologie du fitness. Elle a d\u00e9velopp\u00e9 une application pour les s\u00e9ances d'entra\u00eenement \u00e0 domicile. L'objectif principal de l'application est de proposer des programmes de fitness personnalis\u00e9s et des s\u00e9ances d'entra\u00eenement sans \u00e9quipement. Pour rendre leur application int\u00e9ressante, ils voulaient la doter d'une IA.<\/p>\n<p>Le client n'a pas de d\u00e9partement interne de science des donn\u00e9es. Il recherchait des d\u00e9veloppeurs de r\u00e9seaux neuronaux exp\u00e9riment\u00e9s. C'est la raison pour laquelle il a contact\u00e9 le <a href=\"https:\/\/essidsolutions.com\/\">L'\u00e9quipe ESSID Solutions<\/a>. Ils nous ont demand\u00e9 de d\u00e9velopper des mod\u00e8les de d\u00e9tection d'erreur personnalis\u00e9s et des mod\u00e8les de reconnaissance d'activit\u00e9 avanc\u00e9s.<\/p>\n<h2 style=\"text-align: center;\">D\u00e9fi : d\u00e9veloppement de r\u00e9seaux neuronaux \u00e0 la demande<\/h2>\n<p>Le client pouvait d\u00e9j\u00e0 reconna\u00eetre l'activit\u00e9 humaine dans l'application, mais n'\u00e9tait pas satisfait de sa qualit\u00e9. Elle \u00e9tait lente et impr\u00e9cise. Il nous a donc demand\u00e9 d'am\u00e9liorer la qualit\u00e9 de la reconnaissance des activit\u00e9s gr\u00e2ce \u00e0 l'apprentissage profond.<\/p>\n<p>En ce qui concerne le projet, nos consultants en r\u00e9seaux neuronaux ont sugg\u00e9r\u00e9 les t\u00e2ches de vision par ordinateur suivantes :<\/p>\n<table>\n<tbody>\n<tr>\n<td><strong>Localisation et d\u00e9tection d'objets<\/strong><\/td>\n<td>Nous avons propos\u00e9 d'utiliser des algorithmes de Deep Learning pour une d\u00e9tection pr\u00e9cise et rapide des objets. L'objectif principal \u00e9tait de d\u00e9terminer s'il y avait des objets des vari\u00e9t\u00e9s sp\u00e9cifi\u00e9es (humains, animaux, v\u00e9hicules, etc.) et de d\u00e9terminer leurs limites.<\/td>\n<\/tr>\n<tr>\n<td><strong>Reconna\u00eetre les mouvements et les activit\u00e9s humaines<\/strong><\/td>\n<td>Nous avons d\u00e9cid\u00e9 d'am\u00e9liorer le mouvement humain \u00e0 partir de donn\u00e9es de capteurs \u00e0 l'aide de CNN. Nos experts techniques ont suppos\u00e9 qu'ils \u00e9taient les mieux adapt\u00e9s \u00e0 l'apprentissage de donn\u00e9es provenant d'un capteur (un acc\u00e9l\u00e9rom\u00e8tre de smartphone).<\/td>\n<\/tr>\n<tr>\n<td><strong>Erreur<\/strong><br \/> <strong>D\u00e9tection<\/strong><\/td>\n<td>Notre id\u00e9e \u00e9tait de d\u00e9velopper des algorithmes de d\u00e9tection d'erreurs personnalis\u00e9s. Ils reconna\u00eetraient les erreurs et les impr\u00e9cisions pendant les s\u00e9ances d'entra\u00eenement. Si une erreur est d\u00e9tect\u00e9e, le coach IA dira \u00e0 l'utilisateur de s'exercer de la bonne mani\u00e8re.<\/td>\n<\/tr>\n<tr>\n<td><strong>D\u00e9veloppement multiplateforme<\/strong><\/td>\n<td>Le client avait l'intention de faire fonctionner son application sur diff\u00e9rentes plateformes. Nous avons mis \u00e0 profit notre exp\u00e9rience pratique pour que l'application fonctionne sans probl\u00e8me sur diff\u00e9rents appareils mobiles.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2 style=\"text-align: center;\">Solution : CNN pour la reconnaissance d'activit\u00e9s et la d\u00e9tection d'erreurs<\/h2>\n<p>Pour r\u00e9soudre les t\u00e2ches du projet, nous avons fait des recherches. Nous avons \u00e9tudi\u00e9 les d\u00e9fis du client, les sp\u00e9cificit\u00e9s de l'application et les probl\u00e8mes qu'elle rencontrait.<\/p>\n<p>Nous nous sommes fix\u00e9 les objectifs suivants :<\/p>\n<ul>\n<li>D\u00e9velopper des algorithmes personnalis\u00e9s pour la localisation et la d\u00e9tection d'objets<\/li>\n<li>Am\u00e9liorer la qualit\u00e9 de la reconnaissance des activit\u00e9s<\/li>\n<li>D\u00e9velopper des algorithmes robustes de d\u00e9tection d'erreurs<\/li>\n<li>Stabiliser et optimiser l'architecture du r\u00e9seau neuronal pour que l'application fonctionne rapidement et avec pr\u00e9cision<\/li>\n<li>Faire fonctionner l'application sur diff\u00e9rentes plateformes (d\u00e9veloppement multiplateforme)<\/li>\n<li>Fournir des outils de visualisation sur les CNN pour faciliter la prise de d\u00e9cision<\/li>\n<li>Fournir des scripts et des guides \u00e0 l'\u00e9quipe du client pour faciliter le d\u00e9ploiement sur les appareils mobiles.<\/li>\n<\/ul>\n<p>Un pionnier en mati\u00e8re de <a href=\"https:\/\/essidsolutions.com\/industry\/sport-wellness-ai\">d\u00e9veloppement de projets dans le domaine du fitness et du bien-\u00eatre<\/a>ESSID Solutions a r\u00e9alis\u00e9 avec succ\u00e8s toutes les <a href=\"http:\/\/localhost\/essidsolutions\/service\/computer-vision\">vision par ordinateur<\/a> t\u00e2ches li\u00e9es au projet. Nous avons am\u00e9lior\u00e9 la qualit\u00e9 de la reconnaissance des activit\u00e9s, cr\u00e9\u00e9 des algorithmes de d\u00e9tection d'erreurs \u00e0 partir de z\u00e9ro. Nous avons \u00e9galement effectu\u00e9 d'autres t\u00e2ches qui ont permis d'am\u00e9liorer les performances et la vitesse de l'application.<\/p>\n<h2 style=\"text-align: center;\">R\u00e9sultat : algorithmes personnalis\u00e9s de d\u00e9tection des erreurs, am\u00e9lioration de la reconnaissance des activit\u00e9s.<\/h2>\n<p>L'\u00e9quipe d'ESSID Solutions a appliqu\u00e9 les derni\u00e8res technologies au cas du client. Nous avons fait beaucoup de recherches et de discussions pour r\u00e9pondre aux exigences du client.<\/p>\n<p>Les avantages que le client a retir\u00e9s de sa collaboration avec nous :<\/p>\n<ul>\n<li>la qualit\u00e9 de la reconnaissance des activit\u00e9s a \u00e9t\u00e9 am\u00e9lior\u00e9e par 45%<\/li>\n<li>localisation et d\u00e9tection avanc\u00e9es d'objets<\/li>\n<li>la d\u00e9tection des faux positifs a diminu\u00e9 de 15%<\/li>\n<li>la vitesse globale de l'application a \u00e9t\u00e9 am\u00e9lior\u00e9e de 30%<\/li>\n<li>algorithmes de d\u00e9tection d'erreurs sur mesure<\/li>\n<li>performance de l'application am\u00e9lior\u00e9e par 19%<\/li>\n<li>d\u00e9veloppement multiplateforme (les r\u00e9seaux neuronaux peuvent fonctionner sur diff\u00e9rents appareils mobiles)<\/li>\n<li>Les r\u00e9seaux neuronaux peuvent \u00eatre convertis en CoreML, TFLite, Tensorflow Mobile, MACE, etc.<\/li>\n<\/ul>\n<p>Le client a ainsi obtenu une solution adapt\u00e9e \u00e0 ses besoins. Plus tard, ils ont \u00e0 nouveau contact\u00e9 ESSID Solutions pour des conseils et du d\u00e9veloppement en mati\u00e8re d'apprentissage profond.<\/p>\n<h3 style=\"text-align: center;\">D\u00e9veloppez votre activit\u00e9 avec <a href=\"http:\/\/localhost\/essidsolutions\/service\/machine-learning-consulting\">Solutions d'apprentissage automatique sur mesure<\/a><\/h3>","protected":false},"excerpt":{"rendered":"<p>Principaux d\u00e9tails Am\u00e9lioration des performances de l'application de 19%. D\u00e9fi D\u00e9veloppement de r\u00e9seaux neuronaux \u00e0 la demande Solution CNNs pour la reconnaissance d'activit\u00e9s et la d\u00e9tection d'erreurs Technologies et outils PyTorch, Keras, Tensorflow, CoreML, OpenCV, ONNX Client Le client est une startup sp\u00e9cialis\u00e9e dans le fitness. Elle a d\u00e9velopp\u00e9 une application pour les s\u00e9ances d'entra\u00eenement \u00e0 domicile. L'objectif principal de l'application est de proposer des programmes ... Lire plus <a title=\"Mise \u00e0 l&#039;\u00e9chelle d&#039;une application de fitness avec des r\u00e9seaux neuronaux profonds\" class=\"read-more\" href=\"https:\/\/esisoc.com\/fr\/resource\/developper-une-application-de-fitness-a-laide-de-reseaux-neuronaux-profonds\/\" aria-label=\"Read more about Scaling Up a Fitness App with Deep Neural Networks\">Lire plus<\/a><\/p>","protected":false},"featured_media":2880,"template":"","industry":[40],"expertise":[57,86],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.9 (Yoast SEO v21.9.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Scaling Up a Fitness App with Deep Neural Networks - ESISOC | ESSID Solutions<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/esisoc.com\/fr\/resource\/developper-une-application-de-fitness-a-laide-de-reseaux-neuronaux-profonds\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Scaling Up a Fitness App with Deep Neural Networks\" \/>\n<meta property=\"og:description\" content=\"Key Details Boosted application performance by 19%. 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