strava單車的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列包括價格和評價等資訊懶人包

Cycling Atlas Europe: The 350 Most Beautiful Cycling Routes in Europe

為了解決strava單車的問題，作者Droussent, Claude 這樣論述：

From incredible scenery to dramatic panoramas, these carefully selected tours of the most beautiful places to pedal throughout Europe will delight both seasoned and novice cyclists and feature the groundbreaking user-sourced mapping technology of the Strava app.The guide features 350 of the most

beautiful and scenic cycling routes all over Europe, carefully curated for cyclists of every level and ability--from easy, scenic lowland countryside to visually glorious, more strenuous routes in the Alps and the Carpathians. The book also includes routes that enable the user to re-create itinerari

es from the Tour de France or Giro d’Italia. This is the best European cycling guide ever assembled with the aid of Strava, the mapping app that includes information from the world’s most knowledgeable and expert cyclists who have critically reviewed and rated them. It contains maps, technical detai

ls, tourist information, and tips. Included are urban tours that highlight Europe’s cultural glories and routes that highlight the continent’s breathtaking natural beauty. The book is organized into nine regional sections covering mainland Europe from the British Isles and the Iberian Peninsula to E

astern Europe, Scandinavia, and the boot of Italy.

strava單車進入發燒排行的影片

開發一個具機器學習與智慧分析功能的自行車友互動手機應用程式

為了解決strava單車的問題，作者鍾國辰 這樣論述：

單車早期被視為主要交通工具之一，近幾年已漸漸地演變成最有效的運動方式之一。然而，現有的單車健身行動程式仍存在著許多功能不足的問題，包括相關資料來源分散不全、缺乏多數路段的騎乘資訊、無法根據騎乘記錄判別騎乘能力、缺乏訂定適合訓練菜單之功能、缺乏尋找鄰近且適配車友的功能、缺乏車友互動與結伴出行的功能等。尤其大多數知名運動應用程式皆以歐美地區的資訊服務為主，台灣的路段資訊常會搜尋到過多重複繁雜及品質不佳的自訂路線，也難以尋找到符合自身興趣的路段。因此本論文開發一個單車運動資訊整合手機程式，並提供各種智慧化功能以解決前述問題，首先，利用協同過濾分析使用者和其餘相似車友，並將相似車友喜歡的路段推薦給使

用者本身參考。其次，利用深度學習分析使用者的騎乘強度，並同時作為後續計算參考；再者，利用隨機森林演算法分析使用者各方面的運動強度，並依此推薦訓練菜單給予使用者參考；第四，利用K-means及階層式分群法判斷使用者之間的適配性，協助使用者尋找志同道合的車友；最後，利用Google Map提供車友結伴出行，完成便利的組團出遊功能。

Cycling on Form: A Pro Approach to Speed and Endurance

為了解決strava單車的問題，作者Danielson, Tom/ Danielson 這樣論述：

Cycling on Form reveals the pro approach to cycling training.Riding a bike faster takes more than just fitness. It takes skills that you can master to become a faster, stronger rider. In his new guide, former professional bike racer Tom Danielson shows how to transform your cycling from amateur t

o professional level with the fitness and time you have now.Danielson reveals how the pros go beyond the modern standards of interval workouts, base-building, and recovery to train the whole athlete, mind and body. Danielson shows how to truly ride and train to the fullest through: - Fitness: Self-

tests to identify your riding strengths and weaknesses then focus on custom training to address them.- Mental focus: The pros know that winning means using your head and your legs. You'll get pro tips on big-picture goal setting and mid-ride concentration strategies to help you stay focused.- Execu

tion: Cycling is hard and executing a great ride when it matters takes practice. Danielson shares on-the-bike skills, efficient and powerful techniques, strategies to stay in the race, ways to finish with the lead group, and how to excel on a ride you've never done before. - Nutrition: Pro riders

put sports nutrition into daily practice. Danielson distills the facts of diet, food as fuel, and fueling to recover.Too many cyclists train by trying to set new Strava PRs, only to get frustrated by the limited gains. There's a better way. Cycling On Form unlocks a pro method for riding faster and

stronger.

使用隨機森林預測運動生理負荷： 以自行車運動為例

為了解決strava單車的問題，作者王乾維 這樣論述：

本研究欲使用機器學習，提出一個透過運動生理學的角度，預測運動時的生理負荷之方法，並以自行車運動為例。運動生理學中，衡量生理負荷之方法有基於心率、攝氧量，以及運動自覺量表，本研究欲透過基於心率的心率儲備法，計算生理負荷，提高自行車訓練成效或是避免過高的生理負荷造成的運動傷害。本研究使用之資料來自運動活動紀錄社群STRAVA的開放式資料，將收集之自行車軌跡資料根據坡度分為上坡、平路及下坡三類型，並使用高斯混合模型將各坡度類型做集群，並根據集群結果，使用隨機森林訓練分類器及心率預測模型。基於本研究的結果顯示，透過最適配參數來達到最佳化的模型，其所有分類器模型評測結果，分類的準確率平均為0.9923

5；而所有心率預測模型的決定係數平均為0.6471，絕對均差的平均則是7.4782。在分類上，分類器幾乎能夠準確的預測，而在心率預測上，相較之下，預測的表現並沒那麼出色，其原因可能為資料來自開放式的關係，或是心率傳感器的不同，造成測量誤差，若要實際運用在運動訓練或是預防運動傷害，還稍嫌不足。在未來研究，還需針對本研究不足之處，做建置心率預測模型上的修改，提高預測結果，也期望本研究能夠在提供運動活動記錄與分析之社群服務，有進一步的發展。