diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..f24d933
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,3 @@
+*.ipynb filter=nbstripout
+*.zpln filter=nbstripout
+*.ipynb diff=ipynb
diff --git a/docs/notebooks/gait/gait_analysis.ipynb b/docs/notebooks/gait/gait_analysis.ipynb
index 430f8f4..3ed031e 100644
--- a/docs/notebooks/gait/gait_analysis.ipynb
+++ b/docs/notebooks/gait/gait_analysis.ipynb
@@ -1,171 +1,171 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Gait analysis"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "from paradigma.preprocessing_config import IMUPreprocessingConfig\n",
-        "from paradigma.gait.gait_analysis import extract_gait_features_io, detect_gait_io, extract_arm_activity_features_io, detect_other_arm_activities_io, quantify_arm_swing_io\n",
-        "from paradigma.gait.gait_analysis_config import GaitFeatureExtractionConfig, GaitDetectionConfig, ArmActivityFeatureExtractionConfig, FilteringGaitConfig, ArmSwingQuantificationConfig\n",
-        "from paradigma.imu_preprocessing import preprocess_imu_data_io"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "tags": [
-          "parameters"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "branch = 'gait'\n",
-        "sensor = 'imu'\n",
-        "\n",
-        "path_to_data =  '../../../tests/data'\n",
-        "path_to_classifier = os.path.join(path_to_data, '0.classification', branch)\n",
-        "path_to_sensor_data = os.path.join(path_to_data, '1.sensor_data', sensor)\n",
-        "path_to_preprocessed_data = os.path.join(path_to_data, '2.preprocessed_data', sensor)\n",
-        "path_to_extracted_features = os.path.join(path_to_data, '3.extracted_features', branch)\n",
-        "path_to_predictions = os.path.join(path_to_data, '4.predictions', branch)\n",
-        "path_to_quantification = os.path.join(path_to_data, '5.quantification', branch)\n",
-        "\n",
-        "# Cell has the tag 'parameters', so it won't overwrite the parameters when running the notebook in tests"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Preprocessing"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "config = IMUPreprocessingConfig()\n",
-        "preprocess_imu_data_io(path_to_sensor_data, path_to_preprocessed_data, config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Extract gait features"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "config = GaitFeatureExtractionConfig()\n",
-        "extract_gait_features_io(path_to_preprocessed_data, path_to_extracted_features, config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Detect gait"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "config = GaitDetectionConfig()\n",
-        "detect_gait_io(path_to_extracted_features, path_to_predictions, path_to_classifier, config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Extract arm actvitiy features"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "config = ArmActivityFeatureExtractionConfig()\n",
-        "extract_arm_activity_features_io(path_to_preprocessed_data, path_to_extracted_features, config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Filter gait"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "config = FilteringGaitConfig()\n",
-        "detect_other_arm_activities_io(path_to_extracted_features, path_to_predictions, path_to_classifier, config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Quantify arm swing"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 20,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "config = ArmSwingQuantificationConfig()\n",
-        "quantify_arm_swing_io(path_to_extracted_features, path_to_predictions, path_to_quantification, config)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "paradigma-Fn6RLG4_-py3.11",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11.5"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gait analysis"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from paradigma.preprocessing_config import IMUPreprocessingConfig\n",
+    "from paradigma.gait.gait_analysis import extract_gait_features_io, detect_gait_io, extract_arm_activity_features_io, detect_other_arm_activities_io, quantify_arm_swing_io\n",
+    "from paradigma.gait.gait_analysis_config import GaitFeatureExtractionConfig, GaitDetectionConfig, ArmActivityFeatureExtractionConfig, FilteringGaitConfig, ArmSwingQuantificationConfig\n",
+    "from paradigma.imu_preprocessing import preprocess_imu_data_io"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "branch = 'gait'\n",
+    "sensor = 'imu'\n",
+    "\n",
+    "path_to_data =  '../../../tests/data'\n",
+    "path_to_classifier = os.path.join(path_to_data, '0.classification', branch)\n",
+    "path_to_sensor_data = os.path.join(path_to_data, '1.sensor_data', sensor)\n",
+    "path_to_preprocessed_data = os.path.join(path_to_data, '2.preprocessed_data', sensor)\n",
+    "path_to_extracted_features = os.path.join(path_to_data, '3.extracted_features', branch)\n",
+    "path_to_predictions = os.path.join(path_to_data, '4.predictions', branch)\n",
+    "path_to_quantification = os.path.join(path_to_data, '5.quantification', branch)\n",
+    "\n",
+    "# Cell has the tag 'parameters', so it won't overwrite the parameters when running the notebook in tests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = IMUPreprocessingConfig()\n",
+    "preprocess_imu_data_io(path_to_sensor_data, path_to_preprocessed_data, config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract gait features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = GaitFeatureExtractionConfig()\n",
+    "extract_gait_features_io(path_to_preprocessed_data, path_to_extracted_features, config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Detect gait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = GaitDetectionConfig()\n",
+    "detect_gait_io(path_to_extracted_features, path_to_predictions, path_to_classifier, config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract arm actvitiy features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = ArmActivityFeatureExtractionConfig()\n",
+    "extract_arm_activity_features_io(path_to_preprocessed_data, path_to_extracted_features, config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filter gait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = FilteringGaitConfig()\n",
+    "detect_other_arm_activities_io(path_to_extracted_features, path_to_predictions, path_to_classifier, config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quantify arm swing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = ArmSwingQuantificationConfig()\n",
+    "quantify_arm_swing_io(path_to_extracted_features, path_to_predictions, path_to_quantification, config)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "paradigma-Fn6RLG4_-py3.11",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/poetry.lock b/poetry.lock
index 52a2257..e072b73 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -482,73 +482,73 @@ test = ["pytest"]
 
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 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
 test = ["pep440", "pre-commit", "pytest", "testpath"]
 
+[[package]]
+name = "nbstripout"
+version = "0.7.1"
+description = "Strips outputs from Jupyter and IPython notebooks"
+optional = false
+python-versions = ">=3.8"
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+
+[package.dependencies]
+nbformat = "*"
+
 [[package]]
 name = "nest-asyncio"
 version = "1.6.0"
@@ -1592,13 +1621,13 @@ files = [
 
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-version = "3.4.1"
+version = "3.4.2"
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 [package.extras]
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+dev = ["black", "check-manifest", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pytest-cov", "requests", "rstcheck", "ruff", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "virtualenv", "wheel"]
+test = ["pytest", "pytest-xdist", "setuptools"]
 
 [[package]]
 name = "ptyprocess"
@@ -2890,60 +2920,68 @@ test = ["pytest"]
 
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 name = "sqlalchemy"
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 [package.dependencies]
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 mssql = ["pyodbc"]
 mssql-pymssql = ["pymssql"]
 mssql-pyodbc = ["pyodbc"]
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 ]
 
 [package.dependencies]
@@ -3310,4 +3332,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "bdfb356dba8e5713e2a395befe9448c848d9a874732a04eaa92e4f14247aa027"
+content-hash = "0baac26a2925b5beb6de533239e7a2a45981c10c200fc9c776885a7e9b4fc5f2"
diff --git a/pyproject.toml b/pyproject.toml
index 30d9505..87575c0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,6 +20,7 @@ tsdf = "^0.5.2"
 pytype = "^2024.4.11"
 # for the record: pytype was installed directly with pip (in the poetry environment),
 # because poetry didn't handle the install for different CPU architectures
+nbstripout = "^0.7.1"
 
 [tool.poetry.group.testing.dependencies]
 ipykernel = "^6.27.1"
diff --git a/src/paradigma/gait/feature_extraction.py b/src/paradigma/gait/feature_extraction.py
index 55ad864..c4d50fb 100644
--- a/src/paradigma/gait/feature_extraction.py
+++ b/src/paradigma/gait/feature_extraction.py
@@ -444,75 +444,75 @@ def extract_angle_extremes(
         The extracted angle extremes (peaks)
     """
     # determine peaks
-    df['angle_maxima'] = df.apply(lambda x: find_peaks(x[angle_colname], distance=sampling_frequency * 0.6 / x[dominant_frequency_colname], prominence=2)[0], axis=1)
-    df['angle_minima'] = df.apply(lambda x: find_peaks([-x for x in x[angle_colname]], distance=sampling_frequency * 0.6 / x[dominant_frequency_colname], prominence=2)[0], axis=1) 
+    df[f'{angle_colname}_maxima'] = df.apply(lambda x: find_peaks(x[angle_colname], distance=sampling_frequency * 0.6 / x[dominant_frequency_colname], prominence=2)[0], axis=1)
+    df[f'{angle_colname}_minima'] = df.apply(lambda x: find_peaks([-x for x in x[angle_colname]], distance=sampling_frequency * 0.6 / x[dominant_frequency_colname], prominence=2)[0], axis=1) 
 
-    df['angle_new_minima'] = df['angle_minima'].copy()
-    df['angle_new_maxima'] = df['angle_maxima'].copy()
+    df[f'{angle_colname}_new_minima'] = df[f'{angle_colname}_minima'].copy()
+    df[f'{angle_colname}_new_maxima'] = df[f'{angle_colname}_maxima'].copy()
 
     for index, _ in df.iterrows():
         i_pks = 0                                       # iterable to keep track of consecutive min-min and max-max versus min-max
-        n_min = df.loc[index, 'angle_new_minima'].size  # number of minima in window
-        n_max = df.loc[index, 'angle_new_maxima'].size  # number of maxima in window
+        n_min = df.loc[index, f'{angle_colname}_new_minima'].size  # number of minima in window
+        n_max = df.loc[index, f'{angle_colname}_new_maxima'].size  # number of maxima in window
 
         if n_min > 0 and n_max > 0: 
             # if the first minimum occurs before the first maximum, start with the minimum
-            if df.loc[index, 'angle_new_maxima'][0] > df.loc[index, 'angle_new_minima'][0]: 
+            if df.loc[index, f'{angle_colname}_new_maxima'][0] > df.loc[index, f'{angle_colname}_new_minima'][0]: 
                 # only continue if there are enough minima and maxima to perform operations
-                while i_pks < df.loc[index, 'angle_new_minima'].size - 1 and i_pks < df.loc[index, 'angle_new_maxima'].size: 
+                while i_pks < df.loc[index, f'{angle_colname}_new_minima'].size - 1 and i_pks < df.loc[index, f'{angle_colname}_new_maxima'].size: 
 
                     # if the next minimum comes before the next maximum, we have two minima in a row, and should keep the larger one
-                    if df.loc[index, 'angle_new_minima'][i_pks+1] < df.loc[index, 'angle_new_maxima'][i_pks]: 
+                    if df.loc[index, f'{angle_colname}_new_minima'][i_pks+1] < df.loc[index, f'{angle_colname}_new_maxima'][i_pks]: 
                         # if the next minimum is smaller than the current minimum, keep the next minimum and discard the current minimum
-                        if df.loc[index, angle_colname][df.loc[index, 'angle_new_minima'][i_pks+1]] < df.loc[index, angle_colname][df.loc[index, 'angle_new_minima'][i_pks]]:
-                            df.at[index, 'angle_new_minima'] = np.delete(df.loc[index, 'angle_new_minima'], i_pks)
+                        if df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_minima'][i_pks+1]] < df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_minima'][i_pks]]:
+                            df.at[index, f'{angle_colname}_new_minima'] = np.delete(df.loc[index, f'{angle_colname}_new_minima'], i_pks)
                         # otherwise, keep the current minimum and discard the next minimum
                         else:
-                            df.at[index, 'angle_new_minima'] = np.delete(df.loc[index, 'angle_new_minima'], i_pks+1)
+                            df.at[index, f'{angle_colname}_new_minima'] = np.delete(df.loc[index, f'{angle_colname}_new_minima'], i_pks+1)
                         i_pks -= 1
 
                     # if the current maximum comes before the current minimum, we have two maxima in a row, and should keep the larger one
-                    if i_pks >= 0 and df.loc[index, 'angle_new_minima'][i_pks] > df.loc[index, 'angle_new_maxima'][i_pks]:
+                    if i_pks >= 0 and df.loc[index, f'{angle_colname}_new_minima'][i_pks] > df.loc[index, f'{angle_colname}_new_maxima'][i_pks]:
                         # if the current maximum is smaller than the previous maximum, keep the previous maximum and discard the current maximum
-                        if df.loc[index, angle_colname][df.loc[index, 'angle_new_maxima'][i_pks]] < df.loc[index, angle_colname][df.loc[index, 'angle_new_maxima'][i_pks-1]]:
-                            df.at[index, 'angle_new_maxima'] = np.delete(df.loc[index, 'angle_new_maxima'], i_pks) 
+                        if df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_maxima'][i_pks]] < df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_maxima'][i_pks-1]]:
+                            df.at[index, f'{angle_colname}_new_maxima'] = np.delete(df.loc[index, f'{angle_colname}_new_maxima'], i_pks) 
                         # otherwise, keep the current maximum and discard the previous maximum
                         else:
-                            df.at[index, 'angle_new_maxima'] = np.delete(df.loc[index, 'angle_new_maxima'], i_pks-1) 
+                            df.at[index, f'{angle_colname}_new_maxima'] = np.delete(df.loc[index, f'{angle_colname}_new_maxima'], i_pks-1) 
                         i_pks -= 1
                     i_pks += 1
 
             # or if the first maximum occurs before the first minimum, start with the maximum
-            elif df.loc[index, 'angle_new_maxima'][0] < df.loc[index, 'angle_new_minima'][0]: 
+            elif df.loc[index, f'{angle_colname}_new_maxima'][0] < df.loc[index, f'{angle_colname}_new_minima'][0]: 
                 # only continue if there are enough minima and maxima to perform operations
-                while i_pks < df.loc[index, 'angle_new_minima'].size and i_pks < df.loc[index, 'angle_new_maxima'].size-1:
+                while i_pks < df.loc[index, f'{angle_colname}_new_minima'].size and i_pks < df.loc[index, f'{angle_colname}_new_maxima'].size-1:
                     # if the next maximum comes before the current minimum, we have two maxima in a row, and should keep the larger one
-                    if df.loc[index, 'angle_new_minima'][i_pks] > df.loc[index, 'angle_new_maxima'][i_pks+1]:
+                    if df.loc[index, f'{angle_colname}_new_minima'][i_pks] > df.loc[index, f'{angle_colname}_new_maxima'][i_pks+1]:
                         # if the next maximum is smaller than the current maximum, keep the next maximum and discard the current maximum
-                        if df.loc[index, angle_colname][df.loc[index, 'angle_new_maxima'][i_pks+1]] > df.loc[index, angle_colname][df.loc[index, 'angle_new_maxima'][i_pks]]:
-                            df.at[index, 'angle_new_maxima'] = np.delete(df.loc[index, 'angle_new_maxima'], i_pks) 
+                        if df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_maxima'][i_pks+1]] > df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_maxima'][i_pks]]:
+                            df.at[index, f'{angle_colname}_new_maxima'] = np.delete(df.loc[index, f'{angle_colname}_new_maxima'], i_pks) 
                         # otherwise, keep the current maximum and discard the next maximum
                         else:
-                            df.at[index, 'angle_new_maxima'] = np.delete(df.loc[index, 'angle_new_maxima'], i_pks+1) 
+                            df.at[index, f'{angle_colname}_new_maxima'] = np.delete(df.loc[index, f'{angle_colname}_new_maxima'], i_pks+1) 
                         i_pks -= 1
 
                     # if the current minimum comes before the current maximum, we have two minima in a row, and should keep the larger one
-                    if i_pks > 0 and df.loc[index, 'angle_new_minima'][i_pks] < df.loc[index, 'angle_new_maxima'][i_pks]:
+                    if i_pks > 0 and df.loc[index, f'{angle_colname}_new_minima'][i_pks] < df.loc[index, f'{angle_colname}_new_maxima'][i_pks]:
                         # if the current minimum is smaller than the previous minimum, keep the previous minimum and discard the current minimum
-                        if df.loc[index, angle_colname][df.loc[index, 'angle_new_minima'][i_pks]] < df.loc[index, angle_colname][df.loc[index, 'angle_new_minima'][i_pks-1]]:
-                            df.at[index, 'angle_new_minima'] = np.delete(df.loc[index, 'angle_new_minima'], i_pks-1) 
+                        if df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_minima'][i_pks]] < df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_minima'][i_pks-1]]:
+                            df.at[index, f'{angle_colname}_new_minima'] = np.delete(df.loc[index, f'{angle_colname}_new_minima'], i_pks-1) 
                         # otherwise, keep the current minimum and discard the previous minimum                        
                         else:
-                            df.at[index, 'angle_new_minima'] = np.delete(df.loc[index, 'angle_new_minima'], i_pks) 
+                            df.at[index, f'{angle_colname}_new_minima'] = np.delete(df.loc[index, f'{angle_colname}_new_minima'], i_pks) 
                         i_pks -= 1
                     i_pks += 1
 
     # for some peculiar reason, if a single item remains in the row for angle_new_minima or
     # angle_new_maxima, it could be either a scalar or a vector.
-    for col in ['angle_new_minima', 'angle_new_maxima']:
+    for col in [f'{angle_colname}_new_minima', f'{angle_colname}_new_maxima']:
         df.loc[df.apply(lambda x: type(x[col].tolist())==int, axis=1), col] = df.loc[df.apply(lambda x: type(x[col].tolist())==int, axis=1), col].apply(lambda x: [x])
 
-    df['angle_extrema_values'] = df.apply(lambda x: [x[angle_colname][i] for i in np.concatenate([x['angle_new_minima'], x['angle_new_maxima']])], axis=1) 
+    df[f'{angle_colname}_extrema_values'] = df.apply(lambda x: [x[angle_colname][i] for i in sorted(np.concatenate([x[f'{angle_colname}_new_minima'], x[f'{angle_colname}_new_maxima']]))], axis=1) 
 
     return
 
@@ -583,8 +583,8 @@ def extract_peak_angular_velocity(
     pd.DataFrame
         The dataframe with the forward and backward peak angular velocity
     """
-    df['forward_peak_ang_vel'] = np.empty((len(df), 0)).tolist()
-    df['backward_peak_ang_vel'] = np.empty((len(df), 0)).tolist()
+    df[f'forward_peak_{velocity_colname}'] = np.empty((len(df), 0)).tolist()
+    df[f'backward_peak_{velocity_colname}'] = np.empty((len(df), 0)).tolist()
 
     # for each window
     for index, row in df.iterrows():
@@ -597,13 +597,13 @@ def extract_peak_angular_velocity(
                 # if the peak is a maximum and there is another peak after it
                 if peak_index in row[angle_maxima_colname] and j < len(l_extrema_indices) - 1:
                     # compute the forward peak angular velocity, defined by the maximum negative angular velocity between the two peaks
-                    df.loc[index, 'forward_peak_ang_vel'].append(np.abs(min(row[velocity_colname][l_extrema_indices[j]:l_extrema_indices[j+1]])))
+                    df.loc[index, f'forward_peak_{velocity_colname}'].append(np.abs(min(row[velocity_colname][l_extrema_indices[j]:l_extrema_indices[j+1]])))
                 # if the peak is a minimum and there is another peak after it
                 elif peak_index in row[angle_minima_colname] and j < len(l_extrema_indices) - 1:
                     # compute the backward peak angular velocity, defined by the maximum positive angular velocity between the two peaks
-                    df.loc[index, 'backward_peak_ang_vel'].append(np.abs(max(row[velocity_colname][l_extrema_indices[j]:l_extrema_indices[j+1]])))
+                    df.loc[index, f'backward_peak_{velocity_colname}'].append(np.abs(max(row[velocity_colname][l_extrema_indices[j]:l_extrema_indices[j+1]])))
     
-    return
+    return df
 
 
 def extract_temporal_domain_features(config: IMUConfig, df_windowed:pd.DataFrame, l_gravity_stats=['mean', 'std']) -> pd.DataFrame:
@@ -698,6 +698,6 @@ def extract_spectral_domain_features(config, df_windowed, sensor, l_sensor_colna
 
     df_windowed = pd.concat([df_windowed, cc_cols], axis=1)
 
-    df_windowed = df_windowed.rename(columns={f'cc_{cc_nr}': f'cc_{cc_nr}_{sensor}' for cc_nr in range(1,config.n_coefficients_cc+1)}).rename(columns={'window_start': 'time'})
+    df_windowed = df_windowed.rename(columns={f'cc_{cc_nr}': f'cc_{cc_nr}_{sensor}' for cc_nr in range(1,config.n_coefficients_cc+1)}).rename(columns={'window_start': DataColumns.TIME})
 
     return df_windowed
diff --git a/src/paradigma/gait/gait_analysis.py b/src/paradigma/gait/gait_analysis.py
index a0e94f4..bd13e90 100644
--- a/src/paradigma/gait/gait_analysis.py
+++ b/src/paradigma/gait/gait_analysis.py
@@ -167,25 +167,25 @@ def extract_arm_activity_features(df: pd.DataFrame, config: ArmActivityFeatureEx
     del df
 
     # transform the angle from the temporal domain to the spectral domain using the fast fourier transform
-    df_windowed['angle_freqs'], df_windowed['angle_fft'] = signal_to_ffts(
+    df_windowed[f'{config.angle_colname}_freqs'], df_windowed[f'{config.angle_colname}_fft'] = signal_to_ffts(
         sensor_col=df_windowed[config.angle_colname],
         window_type=config.window_type,
         sampling_frequency=config.sampling_frequency)
 
     # obtain the dominant frequency of the angle signal in the frequency band of interest
     # defined by the highest peak in the power spectrum
-    df_windowed['angle_dominant_frequency'] = df_windowed.apply(
-        lambda x: get_dominant_frequency(signal_ffts=x['angle_fft'],
-                                        signal_freqs=x['angle_freqs'],
+    df_windowed[f'{config.angle_colname}_dominant_frequency'] = df_windowed.apply(
+        lambda x: get_dominant_frequency(signal_ffts=x[f'{config.angle_colname}_fft'],
+                                        signal_freqs=x[f'{config.angle_colname}_freqs'],
                                         fmin=config.power_band_low_frequency,
                                         fmax=config.power_band_high_frequency
                                         ), axis=1
     )
 
-    df_windowed = df_windowed.drop(columns=['angle_fft', 'angle_freqs'])
+    df_windowed = df_windowed.drop(columns=[f'{config.angle_colname}_fft', f'{config.angle_colname}_freqs'])
 
     # compute the percentage of power in the frequency band of interest (i.e., the frequency band of the arm swing)
-    df_windowed['angle_perc_power'] = df_windowed[config.angle_colname].apply(
+    df_windowed[f'{config.angle_colname}_perc_power'] = df_windowed[config.angle_colname].apply(
         lambda x: compute_perc_power(
             sensor_col=x,
             fmin_band=config.power_band_low_frequency,
@@ -197,49 +197,49 @@ def extract_arm_activity_features(df: pd.DataFrame, config: ArmActivityFeatureEx
             )
     )
 
-    # note to eScience: why are the columns 'angle_new_minima', 'angle_new_maxima', 
-    # 'angle_minima_deleted' and 'angle_maxima deleted' created here? Should a copy
+    # note to eScience: why are the columns f'{config.angle_colname}_new_minima', f'{config.angle_colname}_new_maxima', 
+    # f'{config.angle_colname}_minima_deleted' and f'{config.angle_colname}_maxima deleted' created here? Should a copy
     # of 'df_windowed' be created inside 'extract_angle_extremes' to prevent this from
     # happening?
     # determine the extrema (minima and maxima) of the angle signal
     extract_angle_extremes(
         df=df_windowed,
         angle_colname=config.angle_colname,
-        dominant_frequency_colname='angle_dominant_frequency',
+        dominant_frequency_colname=f'{config.angle_colname}_dominant_frequency',
         sampling_frequency=config.sampling_frequency
     )
 
     df_windowed = df_windowed.drop(columns=[config.angle_colname])
 
     # calculate the change in angle between consecutive extrema (minima and maxima) of the angle signal inside the window
-    df_windowed['angle_amplitudes'] = extract_range_of_motion(
-        angle_extrema_values_col=df_windowed['angle_extrema_values']
+    df_windowed[f'{config.angle_colname}_amplitudes'] = extract_range_of_motion(
+        angle_extrema_values_col=df_windowed[f'{config.angle_colname}_extrema_values']
     )
 
-    df_windowed = df_windowed.drop(columns=['angle_extrema_values'])
+    df_windowed = df_windowed.drop(columns=[f'{config.angle_colname}_extrema_values'])
 
     # aggregate the changes in angle between consecutive extrema to obtain the range of motion
-    df_windowed['range_of_motion'] = df_windowed['angle_amplitudes'].apply(lambda x: np.mean(x) if len(x) > 0 else 0).replace(np.nan, 0)
+    df_windowed['range_of_motion'] = df_windowed[f'{config.angle_colname}_amplitudes'].apply(lambda x: np.mean(x) if len(x) > 0 else 0).replace(np.nan, 0)
 
-    df_windowed = df_windowed.drop(columns=['angle_amplitudes'])
+    df_windowed = df_windowed.drop(columns=[f'{config.angle_colname}_amplitudes'])
 
     # compute the forward and backward peak angular velocity using the extrema of the angular velocity
-    extract_peak_angular_velocity(
+    df_windowed = extract_peak_angular_velocity(
         df=df_windowed,
         velocity_colname=config.velocity_colname,
-        angle_minima_colname='angle_minima',
-        angle_maxima_colname='angle_maxima'
+        angle_minima_colname=f'{config.angle_colname}_minima',
+        angle_maxima_colname=f'{config.angle_colname}_maxima'
     )
 
-    df_windowed = df_windowed.drop(columns=['angle_minima','angle_maxima', 'angle_new_minima',
-                                            'angle_new_maxima', config.velocity_colname])
+    df_windowed = df_windowed.drop(columns=[f'{config.angle_colname}_minima',f'{config.angle_colname}_maxima', f'{config.angle_colname}_new_minima',
+                                            f'{config.angle_colname}_new_maxima', config.velocity_colname])
 
     # compute aggregated measures of the peak angular velocity
     for dir in ['forward', 'backward']:
-        df_windowed[f'{dir}_peak_ang_vel_mean'] = df_windowed[f'{dir}_peak_ang_vel'].apply(lambda x: np.mean(x) if len(x) > 0 else 0)
-        df_windowed[f'{dir}_peak_ang_vel_std'] = df_windowed[f'{dir}_peak_ang_vel'].apply(lambda x: np.std(x) if len(x) > 0 else 0)
+        df_windowed[f'{dir}_peak_{config.velocity_colname}_mean'] = df_windowed[f'{dir}_peak_{config.velocity_colname}'].apply(lambda x: np.mean(x) if len(x) > 0 else 0)
+        df_windowed[f'{dir}_peak_{config.velocity_colname}_std'] = df_windowed[f'{dir}_peak_{config.velocity_colname}'].apply(lambda x: np.std(x) if len(x) > 0 else 0)
 
-        df_windowed = df_windowed.drop(columns=[f'{dir}_peak_ang_vel'])
+        df_windowed = df_windowed.drop(columns=[f'{dir}_peak_{config.velocity_colname}'])
 
     # compute statistics of the temporal domain accelerometer signals
     df_windowed = extract_temporal_domain_features(config, df_windowed, l_gravity_stats=['mean', 'std'])
@@ -296,8 +296,8 @@ def detect_other_arm_activities(df: pd.DataFrame, config: FilteringGaitConfig, c
                             [f'{x}_power_above_tremor' for x in config.l_accelerometer_cols] + \
                             [f'cc_{i}_accelerometer' for i in range(1, 13)] + [f'cc_{i}_gyroscope' for i in range(1, 13)] + \
                             [f'grav_{x}_mean' for x in config.l_accelerometer_cols] +  [f'grav_{x}_std' for x in config.l_accelerometer_cols] + \
-                            ['range_of_motion', 'forward_peak_ang_vel_mean', 'backward_peak_ang_vel_mean', 'forward_peak_ang_vel_std', 
-                            'backward_peak_ang_vel_std', 'angle_perc_power', 'angle_dominant_frequency'] + \
+                            ['range_of_motion', f'forward_peak_{config.velocity_colname}_mean', f'backward_peak_{config.velocity_colname}_mean', f'forward_peak_{config.velocity_colname}_std', 
+                            f'backward_peak_{config.velocity_colname}_std', f'{config.angle_colname}_perc_power', f'{config.angle_colname}_dominant_frequency'] + \
                             [f'{x}_dominant_frequency' for x in config.l_accelerometer_cols]
     X = df.loc[:, clf.feature_names_in_]
 
@@ -341,8 +341,8 @@ def quantify_arm_swing(df: pd.DataFrame, config: ArmSwingQuantificationConfig) -
     del df
 
     # create peak angular velocity
-    df_filtered.loc[:, 'peak_ang_vel'] = df_filtered.loc[:, ['forward_peak_ang_vel_mean', 'backward_peak_ang_vel_mean']].mean(axis=1)
-    df_filtered = df_filtered.drop(columns=['forward_peak_ang_vel_mean', 'backward_peak_ang_vel_mean'])
+    df_filtered.loc[:, f'peak_{config.velocity_colname}'] = df_filtered.loc[:, [f'forward_peak_{config.velocity_colname}_mean', f'backward_peak_{config.velocity_colname}_mean']].mean(axis=1)
+    df_filtered = df_filtered.drop(columns=[f'forward_peak_{config.velocity_colname}_mean', f'backward_peak_{config.velocity_colname}_mean'])
 
     # Segmenting
 
@@ -365,7 +365,7 @@ def quantify_arm_swing(df: pd.DataFrame, config: ArmSwingQuantificationConfig) -
         time_colname=DataColumns.TIME,
         segment_nr_colname=DataColumns.SEGMENT_NR,
         window_step_size_s=config.window_step_size,
-        l_metrics=['range_of_motion', 'peak_ang_vel'],
+        l_metrics=['range_of_motion', f'peak_{config.velocity_colname}'],
         l_aggregates=['median'],
         l_quantiles=[0.95]
     )
@@ -394,7 +394,7 @@ def quantify_arm_swing_io(path_to_feature_input: Union[str, Path], path_to_predi
     assert df_features[DataColumns.TIME].equals(df_predictions[DataColumns.TIME])
 
     # Subset features
-    l_feature_cols = [DataColumns.TIME, 'range_of_motion', 'forward_peak_ang_vel_mean', 'backward_peak_ang_vel_mean']
+    l_feature_cols = [DataColumns.TIME, 'range_of_motion', f'forward_peak_{config.velocity_colname}_mean', f'backward_peak_{config.velocity_colname}_mean']
     df_features = df_features[l_feature_cols]
 
     # Concatenate features and predictions
@@ -407,7 +407,7 @@ def quantify_arm_swing_io(path_to_feature_input: Union[str, Path], path_to_predi
     metadata_time.file_name = 'arm_swing_time.bin'
 
     metadata_samples.channels = ['range_of_motion_median', 'range_of_motion_quantile_95',
-                                    'peak_ang_vel_median', 'peak_ang_vel_quantile_95']
+                                    f'peak_{config.velocity_colname}_median', f'peak_{config.velocity_colname}_quantile_95']
     metadata_samples.units = ['deg', 'deg', 'deg/s', 'deg/s']
 
     metadata_time.channels = [DataColumns.TIME, 'segment_duration_ms']
diff --git a/src/paradigma/gait/gait_analysis_config.py b/src/paradigma/gait/gait_analysis_config.py
index f44d0e8..627a0d4 100644
--- a/src/paradigma/gait/gait_analysis_config.py
+++ b/src/paradigma/gait/gait_analysis_config.py
@@ -205,12 +205,12 @@ def __init__(self) -> None:
         self.initialize_column_names()
 
         self.d_channels_values = {
-            "angle_perc_power": "proportion",
+            f"{self.angle_colname}_perc_power": "proportion",
             "range_of_motion": "deg",
-            "forward_peak_ang_vel_mean": DataUnits.ROTATION,
-            "forward_peak_ang_vel_std": DataUnits.ROTATION,
-            "backward_peak_ang_vel_mean": DataUnits.ROTATION,
-            "backward_peak_ang_vel_std": DataUnits.ROTATION,
+            f"forward_peak_{self.velocity_colname}_mean": DataUnits.ROTATION,
+            f"forward_peak_{self.velocity_colname}_std": DataUnits.ROTATION,
+            f"backward_peak_{self.velocity_colname}_mean": DataUnits.ROTATION,
+            f"backward_peak_{self.velocity_colname}_std": DataUnits.ROTATION,
             "std_norm_acc": DataUnits.GRAVITY,
             "grav_accelerometer_x_mean": DataUnits.GRAVITY,
             "grav_accelerometer_x_std": DataUnits.GRAVITY,
@@ -233,7 +233,7 @@ def __init__(self) -> None:
             "accelerometer_z_power_tremor": "X",
             "accelerometer_z_power_above_tremor": "X",
             "accelerometer_z_dominant_frequency": DataUnits.FREQUENCY,
-            "angle_dominant_frequency": DataUnits.FREQUENCY,
+            f"{self.angle_colname}_dominant_frequency": DataUnits.FREQUENCY,
         }
 
         for sensor in ["accelerometer", "gyroscope"]:
@@ -243,11 +243,19 @@ def __init__(self) -> None:
 
 class FilteringGaitConfig(IMUConfig):
 
+    def initialize_column_names(
+        self
+    ) -> None:
+        
+        self.angle_colname=DataColumns.ANGLE
+        self.velocity_colname=DataColumns.VELOCITY
+
     def __init__(self) -> None:
         super().__init__()
         self.classifier_file_name = "gait_filtering_classifier.pkl"
 
         self.set_filenames_values("arm_activity")
+        self.initialize_column_names()
 
 
 
@@ -257,6 +265,8 @@ def __init__(self) -> None:
         super().__init__()
         self.set_filenames_values("arm_activity")
 
+        self.angle_colname = DataColumns.ANGLE
+        self.velocity_colname = DataColumns.VELOCITY
         self.pred_other_arm_activity_proba_colname = DataColumns.PRED_OTHER_ARM_ACTIVITY_PROBA
         self.pred_other_arm_activity_colname = DataColumns.PRED_OTHER_ARM_ACTIVITY
 
diff --git a/tests/data/3.extracted_features/gait/arm_activity_meta.json b/tests/data/3.extracted_features/gait/arm_activity_meta.json
index 4a6c7a5..2c60fd9 100644
--- a/tests/data/3.extracted_features/gait/arm_activity_meta.json
+++ b/tests/data/3.extracted_features/gait/arm_activity_meta.json
@@ -28,10 +28,10 @@
             "channels": [
                 "angle_perc_power",
                 "range_of_motion",
-                "forward_peak_ang_vel_mean",
-                "forward_peak_ang_vel_std",
-                "backward_peak_ang_vel_mean",
-                "backward_peak_ang_vel_std",
+                "forward_peak_velocity_mean",
+                "forward_peak_velocity_std",
+                "backward_peak_velocity_mean",
+                "backward_peak_velocity_std",
                 "std_norm_acc",
                 "grav_accelerometer_x_mean",
                 "grav_accelerometer_x_std",
diff --git a/tests/data/3.extracted_features/gait/arm_activity_values.bin b/tests/data/3.extracted_features/gait/arm_activity_values.bin
index 496cd84..35ae06d 100644
Binary files a/tests/data/3.extracted_features/gait/arm_activity_values.bin and b/tests/data/3.extracted_features/gait/arm_activity_values.bin differ
diff --git a/tests/data/5.quantification/gait/arm_swing_meta.json b/tests/data/5.quantification/gait/arm_swing_meta.json
index a4fce64..af7f641 100644
--- a/tests/data/5.quantification/gait/arm_swing_meta.json
+++ b/tests/data/5.quantification/gait/arm_swing_meta.json
@@ -30,8 +30,8 @@
             "channels": [
                 "range_of_motion_median",
                 "range_of_motion_quantile_95",
-                "peak_ang_vel_median",
-                "peak_ang_vel_quantile_95"
+                "peak_velocity_median",
+                "peak_velocity_quantile_95"
             ],
             "units": [
                 "deg",