From 9d3334c78b382d08a8afe4a7889e98dfa620e10b Mon Sep 17 00:00:00 2001
From: Matteo Fasulo <74818541+MatteoFasulo@users.noreply.github.com>
Date: Fri, 27 Feb 2026 12:29:55 +0100
Subject: [PATCH 1/2] Update hand gesture classification experiment details

---
 docs/model/TinyMyo.md | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/docs/model/TinyMyo.md b/docs/model/TinyMyo.md
index 35a332d..281759e 100644
--- a/docs/model/TinyMyo.md
+++ b/docs/model/TinyMyo.md
@@ -113,9 +113,9 @@ TinyMyo supports three major categories:
 
 Evaluated on:
 
-* **Ninapro DB5** (52 classes, 10 subjects)
-* **EPN-612** (5 classes, 612 subjects)
-* **UCI EMG** (6 classes, 36 subjects)
+* **Ninapro DB5** (52 classes, 10 subjects, 200 Hz)
+* **EPN-612** (5 classes, 612 subjects, 200 Hz)
+* **UCI EMG** (6 classes, 36 subjects, 200 Hz)
 * **Generic Neuromotor Interface** (Meta wristband; 9 gestures)
   * Repository: [MatteoFasulo/generic-neuromotor-interface](https://github.com/MatteoFasulo/generic-neuromotor-interface)
 
@@ -126,8 +126,8 @@ Evaluated on:
 * EMG filtering: **20–90 Hz** bandpass + 50 Hz notch
 * Windows:
 
-  * **200 ms** (best for DB5)
-  * **1000 ms** (best for EPN & UCI)
+  * **1 sec** (best for DB5)
+  * **5 sec** (best for EPN & UCI)
 * Per-channel z-scoring
 * Linear classification head
 
@@ -138,9 +138,9 @@ Evaluated on:
 
 | Dataset                  | Metric   | Result            |
 | ------------------------ | -------- | ----------------- |
-| **Ninapro DB5 (200 ms)** | Accuracy | **89.41 ± 0.16%** |
-| **EPN-612 (1000 ms)**    | Accuracy | **96.74 ± 0.09%** |
-| **UCI EMG (1000 ms)**    | Accuracy | **97.56 ± 0.32%** |
+| **Ninapro DB5 (1 sec)** | Accuracy | **89.41 ± 0.16%** |
+| **EPN-612 (5 sec)**    | Accuracy | **96.74 ± 0.09%** |
+| **UCI EMG (5 sec)**    | Accuracy | **97.56 ± 0.32%** |
 | **Neuromotor Interface** | CLER     | **0.153 ± 0.006** |
 
 TinyMyo achieves **state-of-the-art** on DB5, EPN-612, and UCI.
@@ -151,7 +151,7 @@ TinyMyo achieves **state-of-the-art** on DB5, EPN-612, and UCI.
 
 Dataset: **Ninapro DB8**
 Task: Regress **5 joint angles (DoA)**
-Preprocessing: z-score only; windows of **200 ms** or **1000 ms**
+Preprocessing: z-score only; windows of **100 ms** or **500 ms**
 
 **Regression head (788k params)**
 
@@ -219,7 +219,7 @@ Key elements:
 * Integer softmax, integer LayerNorm, integer GELU
 * Static liveness-based memory arena
 
-**Runtime (NinaPro EPN612 pipeline):**
+**Runtime (EPN612 dataset):**
 
 * **0.785 s inference time**
 * **44.91 mJ energy**

From 28a362e3383ececca257f72b582040cbf67672b9 Mon Sep 17 00:00:00 2001
From: Matteo Fasulo <74818541+MatteoFasulo@users.noreply.github.com>
Date: Fri, 27 Feb 2026 13:29:08 +0100
Subject: [PATCH 2/2] Clarify dataset and performance metrics in TinyMyo.md

Updated dataset information and performance metrics for hand kinematic regression.
---
 docs/model/TinyMyo.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/model/TinyMyo.md b/docs/model/TinyMyo.md
index 281759e..06a52bc 100644
--- a/docs/model/TinyMyo.md
+++ b/docs/model/TinyMyo.md
@@ -149,7 +149,7 @@ TinyMyo achieves **state-of-the-art** on DB5, EPN-612, and UCI.
 
 ### **4.2 Hand Kinematic Regression**
 
-Dataset: **Ninapro DB8**
+Dataset: **Ninapro DB8** (2000 Hz)
 Task: Regress **5 joint angles (DoA)**
 Preprocessing: z-score only; windows of **100 ms** or **500 ms**
 
@@ -162,7 +162,7 @@ Preprocessing: z-score only; windows of **100 ms** or **500 ms**
 
 **Performance (Fine-tuned)**
 
-* **MAE = 8.77 ± 0.12°** (1000 ms window)
+* **MAE = 8.77 ± 0.12°** (500 ms window)
 
 Although previous works achieve lower MAE (≈6.89°), those models are **subject-specific**, whereas TinyMyo trains **one model across all subjects**, a significantly harder problem.