我們提出的意圖辨識及控制方法可以有效幫助銀髮族在移動上更加安全省 力，然而目前所設計的模組，在意圖辨識系統的穩定度及透過意圖控制i-go移動 上仍有改進的空間，茲討論如下：
1. 意圖辨識握把的調整：目前所設計的握把尺寸長度是120mm，不過根據我們 實驗後發現，手掌如此大的人屬於少數，對大部份的使用者來說120mm還是 太長了，比較適合大部份使用者的長度目前認定是100mm，且握把機構可改 良製作成對其骨幹軸心可旋轉，如此在辨識意圖上可以獲得更明確的數值。
2. 力感應器的種類：目前使用的力感應器量測範圍為0lb~25lb，但經過我們的 實驗，一般常見的意圖，其大小僅在0lb~5lb之間，因此我們用的力感應器在 解析度上會比較差，然而Tekscan公司較小一級的感測器範圍為0lb~1lb，這 樣又太小，如果能找到適合範圍的感應器對意圖辨識會有更好的效果。
3. 濾波電路的設計：由於i-go系統結構越來越複雜，許多訊號間不免會產生干 擾，會使力感應器經過A/D轉換後的數值產生誤差，儘管可以用軟體補償的 方式降低誤差量，但是若能從硬體上著手加強，會使系統運作更順暢、對意 圖的辨識也能更精確。
4. 意圖辨識演算法：目前的意圖判斷主要是針對轉彎、前進、緊急煞車等意圖，
希望未來能加入跌倒意圖的判定，讓銀髮族在即將跌倒的時候能提供煞車 力，防止其跌倒。
未來期待將 i-go 及本論文提出的方法廣泛運用在醫院及安養中心，取代輪椅，
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