Different Features of Health and Fitness app

Workout and exercise

                        This features includes workout plans and videos with explanatory comments. App should be able to create an individual training plan depending on the goals and initial data, as well as easily monitor the athlete’s progress.

App provides information about different exercise with images and  videos on how to perform exercises. It also provided logging features were users will be able to log their work out session. 

Personal trainer apps offer a set of tailormade exercises by enabling the user to choose the difficulty level and the type of exercises that they like the most. 

Most fitness apps come with AI-enabled coaches who can guide you to do your exercises in the right manner. These trainers act like human trainers and can also give you tips to build your strength and stamina. 

Yoga and Meditation

This category includes meditation, yoga, and exercise apps with spiritual practices. The meditation applications are useful to calm down an anxious mind by offering guided & unstructured video.

It may consist of guided meditation sessions, nature sounds, and several step-by-step processes to stay in a peaceful and happy space.

With Yoga feature provides list of Yoga images and videos which can be done at home. 

Diet and Nutrition

    These applications help users control their weight by counting calories consumed and burned, controlling water balance, and encouraging healthy eating habits.
The app will help to create grocery shopping lists, and even collect healthy food recipes.

This will help them in the realtime tracking of their calorie intake and the exact requirement as per their body.

With this food logging app, the users can enter the information of what they had for meals throughout the day and they can see how many calories they have consumed. This way they can have firm control over the calorie intake.

 With this feature, you can present the user with specialized diet tips fit for their eating habits. You can also add recipes that are not only healthy but also tasty as well, motivating the user to follow a healthier eating habit.

Activity tracking

Such applications can count the number of steps and count calories. With geolocation, they can track distance walked.
Users can track their progress of activities on a daily, weekly, or monthly basis and share the information over social media. They can share their workout hours, distance covered, calories burnt, and more on different social media platforms.

This category includes apps that track physical activity, including steps taken, stairs climbed, hours slept, distance traveled, and calories burned.

The wearable device integration is absolutely important for the workout app because it is the best way to track the progress of an exercise routine. The user can track their heart rate, calories burned, goal progress, etc. with the wearable device integration, you can make it convenient for the users to use your app.

Notification & Reminders

Anyone who is working out regularly would never want to miss a session. However, keeping up with the busy lives; sometimes, it does slips out of our mind. In such cases a push notification from the app can be very useful to remind us of our workout session.
Any individual who is working out consistently will never wish to miss a session.

Online consultation & chat

A lot of time, a workout app is not enough. This is why you need to give the users an option to connect and chat with personal trainers near them. By having this network of gym specialists and personal trainers, you will give the users access to better exercise and training experience. Along with this, you can add the workout checklist app feature to it where the user can make their own workout checklist to follow.

Summary of the Features of Health and Fitness app

• Workout and exercise
• Yoga and Meditation
• Diet and Nutrition
• Activity tracking
• Notification & Reminders
• Online consultation & chat

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Different sensors used in fitness trackers

Different sensors used in fitness trackers   

          Smartwatches with Fitness trackers have become essential lifestyle devices that helps track how active you are along with basic health parameters.

The smartwatches do a lot more than showing time, show notifications on your wrist. They're everything in one: a fitness tracker, a wallet, and in some cases, even making phone calls.

            A smartwatch can also save your life with elevated heart rate alerts and automatically connect you to emergency services if you fall. Electrocardiogram (ECG) and blood oxygen (SpO2) readings are perks of certain premium models, too. It still has a compass, fall detection and GPS.

Following are the different sensors used in fitness trackers:

Ambient light sensor: This sensor is also used in mobile devices to increase or decrease the screen brightness, thus saving your battery life. This sensor tweaks display brightness based on the surrounding light for better battery life.

3 axis accelerometer: This is an electromechanical device that senses gravity, as well as linear accelerations, detects movement, and tracks direction. This track's forward and backward movement by sensing gravity and determine the body’s orientation, position, and also rate of change of speed.

Altimeter: This sensor uses atmospheric pressure to sense any changes in the altitude. Detects Change in height, are you climbing stairs or going down to calculate calories count. 

Barometer : This measures and shows the atmospheric pressure, thus you can know whether it is going to be sunny or rainy day.

Best Fitness Tracker and Smart Watch

Optical heart rate: This sensor detects heartbeats per minute. The device with an optical HR sensor calculates your heartbeat  per minute using a special algorithm. This sensor uses light to check the speed of blood flow on the wrist. When the heartbeats, blood moves quickly inside thus less light is reflected back to the sensor and is detected as a heartbeat 

SpO2: This sensor used to monitor and measure blood oxygen levels. It uses LEDs as a light source to emit light into the tissue and a photodetector is used to collect the light back from the skin and measure how well the oxygen is supplied to each part of your body from your heart. 

Bioimpedance sensor: This sensor is used in Smart weighing scales to measure your body composition such as the total body fat w.r.t your lean body mass.

Proximity sensor : These sensors saves battery and wakes the display when needed.  It helps in lowering the power consumption by putting the device to sleep If you are not wearing the fitness tracker, this sensor enables the device to sleep and save battery when not in use and turns off display

Compass : It helps in direction and Maps. It shows the direction of magnetic north and bearings from it. A compass helps Map applications to run on a smartwatch and also gives the device a sense of direction.

ECG sensor: This sensor is to detect the minute electrical impulse that your heart sends out with every heartbeat This sensor detects this minute heart signal through the electrodes on the wearable.

GPS : It works by tracking your exact location while you are walking or running by calculating the distance between series of GPS satellites. It helps in detecting how much you are running, the location of wearable, and tracks your activity.

LTE : A LTE enabled device, simply means that is has built-in mobile connection i.e., you can make/receive calls directly from the device itself.

Gyroscope : Used to detect motion . It is used with other sensors to determine whether you are actually running or simply jogging.

UV sensor: It measures exposure to harmful sunlight. The UV sensor monitors the sunlight and alerts when you’re absorbing dangerous UV radiation whether the sunlight outdoors is harmful or not.

Magnetometer :  The sensor measures the magnetic field of the earth and can also be used as a compass. Works along with the GPS and compass to determine the exact  coordinates of your location 

Electrodermal activity sensor : It measure stress along with a heart rate tracker, ECG, and skin temperature sensor. It detects small electrical changes in the sweat level of your skin and helps you manage your stress.

Best Fitness Tracker and Smart Watch

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Best Machine learning books

        

                  Machine learning and artificial intelligence are growing fields and growing topics of study.  In this article, we’ll review some of the most popular resources for machine learning beginners. Some of these books will require familiarity with some coding languages and math.

1. Machine Learning For Dummies” by John Paul Mueller and Luca Massaron

Topics Covered :

• Introducing How Machines Learn : the Real Story about AI, Learning in the Age of Big Data, Glance at the Future

• Preparing Your Learning Tools : Installing an R, Python , Coding in R Using RStudio, Python Using Anaconda and exploring other machine learning tools

• Getting Started with the Math Basics : Math Behind Machine Learning, Probabilities, Statistics,Cost Functions, Error Curve , Greedy Classification Trees, Incredible Perceptron, Greedy Classification Trees. Validating Machine Learning : Training, Validating, and Testing

• Learning from Smart and Big Data : Preprocessing Data, Leveraging Similarity , Working with Linear Models, Neural Networks, Support Vector Machines

• Applying Learning to Real Problems : Classifying Images, Scoring Opinions and Sentiments, Recommending Products and Movies

Where to buy: Amazon

2. Programming Collective Intelligence” by Toby Segaran

Topics Covered :

• Introduction to Collective Intelligence, Making Recommendations

• Discovering Groups : Supervised versus Unsupervised Learning, Word Vectors, Hierarchical Clustering, K-Means Clustering, Viewing Data in Two Dimensions

• Searching and Ranking, Optimization

• Document Filtering : Filtering Spam, A Naïve Classifier, The Fisher Method, Modeling with Decision Trees

• Building Price Models :k-Nearest Neighbors ,Weighted Neighbors

• Advanced Classification: Understanding Kernel Methods and SVMs , LIBSVM , Finding Independent Features

• Algorithm Summary : Bayesian Classifier, Decision Tree Classifier, Neural Networks, Support-Vector Machines, k-Nearest Neighbors, Clustering, Multidimensional Scaling, Non-Negative Matrix Factorization, Optimization

• Different third party libraries and Mathematical Formulas

Where to buy: Amazon

3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Topics Covered :

• What Machine Learning is, what problems it tries to solve, and the main categories and fundamental concepts of its systems, Optimizing a cost function, Handling, cleaning, and preparing data, Selecting a model, The challenges of Machine Learning

• The most common learning algorithms: Linear and Polynomial Regression, Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Ensemble methods

• unsupervised learning techniques, including clustering, density estimation, and anomaly detection

• What neural nets are and what they’re good for, Building and training neural nets using TensorFlow and Keras

• Neural net architectures: feedforward neural nets for tabular data, convolutional nets for computer vision, recurrent nets and long short-term memory (LSTM) nets for sequence processing, encoder/decoders and Transformers for natural language processing, autoencoders and generative adversarial networks (GANs) for generative learning

• Techniques for training deep neural nets, Training and deploying TensorFlow models at scale 

Where to buy: Amazon

4. Natural Language Processing with Python

Topics Covered :

• Natural Language Processing with Python and spaCy will show you how to create NLP applications like chatbots, text-condensing scripts, and order-processing tools quickly and easily

• Using Python for Natural Language Processing

• The spaCy Library, How Can Computers Understand Language?, Using Machine Learning for Natural Language Processing

• Neural Network Models, Convolutional Neural Networks for NLP,

• Extracting and Generating Text with Part-of-Speech Tags,, Finding Patterns Based on Linguistic Features

• Implementing and Deploying a Chatbot Works

Where to buy: Amazon

5. Machine Learning in Action

Topics Covered :

• Machine learning basics, How to choose the right algorithm, python, NumPy library, Classifying with k-Nearest Neighbors

• Decision trees, probability theory: naïve Bayes

• Logistic regression, Support vector machines, AdaBoost meta-algorithm, linear regression

• Tree-based regression, Unsupervised learning

• Big data and MapReduce Hadoop Streaming, Machine learning in MapReduce, The Pegasos algorithm

Where to buy: Amazon

6. Machine Learning with TensorFlow

Topics Covered :

• Machine-learning fundamentals, Types of learning : Supervised learning, Unsupervised learning, Reinforcement learning, TensorFlow essentials

• Core learning algorithms Linear regression,Polynomial model, Regularization, Using logistic regression, Multiclass classifier

• Automatically clustering data : K-means clustering, Clustering using a self-organizing map

• Hidden Markov models, The neural network paradigm : Reinforcement learning, Convolutional neural networks, Recurrent neural networks

• Sequence-to-sequence models for chatbots

Where to buy: Amazon

7. Introduction to Machine Learning with Python: A Guide for Data Scientists

Topics Covered :

• Scikit-learn, Jupyter Notebook, NumPy, SciPy, matplotlib, pandas, mglearn

• Supervised Machine Learning Algorithms : k-Nearest Neighbors, Linear Models, Naive Bayes Classifiers, Decision Trees, Kernelized Support Vector Machines, Neural Networks (Deep Learning)

• Unsupervised Learning and Preprocessing :  Dimensionality Reduction, Feature Extraction, and Manifold Learning, Clustering

• Representing Data and Engineering Features, Model Evaluation and Improvement, Algorithm Chains and Pipelines, Working with Text Data

Where to buy: Amazon

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