Adding files

mpq/elderly_fall/elderly_fall.py

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+import init_utils
+import common
+
+# Initialize the environment and get the name
+name = init_utils.initialize_environment(__file__)
+args = init_utils.get_args()
+
+# Set arguments from command line
+max_acc_drop = args.max_acc_drop
+device = args.device
+
+import pandas as pd 
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from sklearn.model_selection import train_test_split
+
+# Load our Dataset
+
+pelvis_acc = pd.read_excel('elderly_fall/Posterior Pelvis Accelerometer Measures.xlsx',
+                   sheet_name = 'ST')
+
+y_data = pelvis_acc['Faller (1), Non-Faller (0)'].to_numpy()
+
+pelvis_acc = pelvis_acc.drop(columns = [pelvis_acc.columns[0], 'Faller (1), Non-Faller (0)'])
+
+head_acc = pd.read_excel('elderly_fall/Head Accelerometer Measures.xlsx',
+                    sheet_name = 'ST')
+
+head_acc = head_acc.drop(columns = [head_acc.columns[0], 'Faller (1), Non-Faller (0)'])
+
+
+pressure_sen = pd.read_excel('elderly_fall/Pressure-Sensing Insole Measures.xlsx',
+                   sheet_name = 'ST')
+
+pressure_sen = pressure_sen.drop(columns = [pressure_sen.columns[0], 'Faller (1), Non-Faller (0)'])
+
+
+left_shank = pd.read_excel('elderly_fall/Left Shank Accelerometer Measures.xlsx',
+                   sheet_name = 'ST')
+
+left_shank = left_shank.drop(columns = [left_shank.columns[0], 'Faller (1), Non-Faller (0)'])
+
+
+X_data = pd.concat([head_acc, pressure_sen, pelvis_acc, left_shank], axis = 1)
+
+# Preprocess the Data
+
+def normalize_column(column):
+    old_min = column.min()
+    old_max = column.max()
+    new_min = 0
+    new_max = 255
+    normalized_column = ((column - old_min) / (old_max - old_min)) * (new_max - new_min) + new_min
+    return normalized_column.astype(int)
+
+X_data = X_data.apply(normalize_column)
+X_d = X_data/255.0
+X_train, X_test, y_train, y_test = train_test_split(X_d, y_data, 
+                        test_size = 0.15, random_state = 42)
+
+X_train = X_train.to_numpy()
+X_test = X_test.to_numpy()
+
+BATCH_SIZE = 1
+epochs = 5
+lr = 0.0001
+
+class EF_MLP(nn.Module):
+    def __init__(self):
+        super(EF_MLP, self).__init__()
+        self.linear1 = nn.Linear(117, 20)
+        self.relu1 = nn.ReLU()
+        self.linear2 = nn.Linear(20, 2)
+        
+    def forward(self,X):
+        X = self.relu1(self.linear1(X))
+        X = self.linear2(X)
+        return F.log_softmax(X, dim = 1)
+    
+net = EF_MLP()
+
+common.create_ibex_qnn(net, name, device, X_train, y_train, X_test, y_test, BATCH_SIZE = BATCH_SIZE, 
+                 epochs = epochs, lr = lr, max_acc_drop = max_acc_drop)