Human Activity Recognition using Smartphone Data with Machine Learning

Human Activity Recognition using Smartphone Data

Human activity recognition is the problem of classifying sequences of data recorded by specialized harnesses or smart phones into known well-defined Human activities.

It is a challenging problem as the large number of observations are produced each second, the temporal nature of the observations, and the lack of a clear way to relate data to known movements increase the challenges.

In this Machine Learning Project, we will create a model for recognition of human activity using the smartphone data.

Let’s start with Importing necessary libraries

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import warnings

Download the data sets

Reading the data

train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")

To Combine both the  data frames

train['Data'] = 'Train'
test['Data'] = 'Test'
both = pd.concat([train, test], axis=0).reset_index(drop=True)
both['subject'] = '#' + both['subject'].astype(str)
train.shape, test.shape

((7352, 564), (2947, 564))

float64    561
object       3
dtype: int64
def basic_details(df):
    b = pd.DataFrame()
    b['Missing value'] = df.isnull().sum()
    b['N unique value'] = df.nunique()
    b['dtype'] = df.dtypes
    return b
activity = both['Activity']
label_counts = activity.value_counts()

plt.figure(figsize= (12, 8)), label_counts)
Data = both['Data']
Subject = both['subject']
train = both.copy()
train = train.drop(['Data','subject','Activity'], axis =1)

To Scale the data

# Standard Scaler
from sklearn.preprocessing import StandardScaler
slc = StandardScaler()
train = slc.fit_transform(train)

# dimensionality reduction
from sklearn.decomposition import PCA
pca = PCA(n_components=0.9, random_state=0)
train = pca.fit_transform(train)

Splitting the  data into training and testing

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(train, activity, test_size = 0.2, random_state = 0)

Test options and evaluation metric

num_folds = 10
seed = 0
scoring = 'accuracy'
results = {}
accuracy = {}

Activity Recognition Algorithm

# Finalizing the model and comparing the test, predict results
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
from sklearn.model_selection import KFold, cross_val_score
model = KNeighborsClassifier(algorithm= 'auto', n_neighbors= 8, p= 1, weights= 'distance')

_ = cross_val_score(model, X_train, y_train, cv=10, scoring=scoring)
results["GScv"] = (_.mean(), _.std()), y_train) 
y_predict = model.predict(X_test)

accuracy["GScv"] = accuracy_score(y_test, y_predict)

print(classification_report(y_test, y_predict))

cm= confusion_matrix(y_test, y_predict)
sns.heatmap(cm, annot=True)
                      precision    recall  f1-score   support

            LAYING       1.00      1.00      1.00       377
           SITTING       0.92      0.87      0.90       364
          STANDING       0.89      0.93      0.91       390
           WALKING       0.96      0.99      0.97       335
WALKING_DOWNSTAIRS       0.99      0.95      0.97       278
  WALKING_UPSTAIRS       0.98      0.98      0.98       316

          accuracy                           0.95      2060
         macro avg       0.96      0.95      0.95      2060
      weighted avg       0.95      0.95      0.95      2060

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Aman Kharwal

I am a programmer from India, and I am here to guide you with Data Science, Machine Learning, Python, and C++ for free. I hope you will learn a lot in your journey towards Coding, Machine Learning and Artificial Intelligence with me.



  2. […] Human activity recognition is the problem of classifying sequences of data recorded by specialized harnesses or smart phones into known well-defined Human activities. See Complete Project. […]

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