The document outlines the architecture of an automated personal training system called Muvr, which uses various technologies including iOS, Akka, Spark, Neon, and Cassandra to deliver real-time personalized exercise recommendations and monitor user performance. It details the system's capabilities, such as exercise session suggestions, proper form tips, and machine learning applications for continuous improvement. It also discusses deployment strategies using Docker and resource management on AWS for cost-effectiveness.

1. Real-time personal trainer on
the SMACK stack
@honzam399 Jan Machacek
@anirvan_c Anirvan Chakraborty

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Automated personal trainer - muvr
• Suggests the sequence of exercise sessions
• Suggests exercises in a session, including exercise
parameters (e.g. weight, repetitions, …)
• Provides tips on proper exercise form
• With additional hardware (smartwatch, smart clothes),
muvr provides
• Completely unobtrusive exercise experience
• More accurate tips on proper exercise form
• With over–fitting, it is usable for physiotherapy

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Architecture

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Privacy

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The technologies—iOS
• Learns the users’ behaviour
• Exercise sessions
• Exercises within exercise session
• Short–term prediction of [scalar] labels for the exercises
• Performs the real–time analysis of the incoming sensor
data
• Advised by the expected behaviour
• Signal processing to compute repetitions / strokes
• Forward–propagation to label the exercise
• Submits all recorded sensor data and confirmed (!) labels
per session
• Handles offline / travel modes
• Synchronises the data across the user’s devices using iCloud

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The technologies—Akka
• Reactive services for user profiles, model parameters,
and sensor data
• CQRS/ES implementation, which helps to
• Handle peaks in load
• Handle failures of individual nodes
• Reason about the scope of the mutable state we keep
• Uses Cassandra for its journal and snapshot stores
• The written values are binary “blobs”
• Writes the sensor data to Cassandra
• Writes the sensor data in “readable” form; it can be read outside the Akka / Scala
world
• Reads the model and exercise parameters from
Cassandra
• It selects the best / newest model parameters to serve to the mobile app

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The technologies—Spark
• Distributed computation framework
• “Big data” tasks
• Integrates extremely well with Cassandra
• Reads and processes the profiles and sensor data
• Identifies clusters of users on their profile information
• Slices the sensor inputs by sensor types
• Writes the results to another store
• Runs in batches
• Executes by schedule (typically once a day)

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The technologies—neon
• A machine learning framework, including
• “The usual” suspects in tensor algebra
• Signal processing
• Different ML approaches
• Training and evaluation programs
• Both programs terminate either upon discovering the perfect model or when their
budget is up
• Reads clustered training and testing data from the Spark job
• Writes the model parameters and evaluation result to Cassandra

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The technologies—Cassandra
• Underpins the entire platform
• Journal and snapshot store for Akka
• Sensor data store
• Model parameter store
• “Summary” store
• High availability
• No single point of failure
• High read and write
• Replication factor
• Tuneable consistency level

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Spark & Cassandra
• Group the sensor data into n clusters by user profile with
biometric ID
• Expand the sensor data
• Slices of the sensor data by combinations of accelerometer, gyroscope, heart rate,
targeted muscle group strain gauges, …
• 1 user = 1 MiB from one sensor per hour; but 4 sensors expand into 4! MiB
• Trivial tasks
• The most popular user–contributed exercises
• The most popular exercise sessions and exercises within the sessions
• The most effective (by overall fitness improvement, weight loss, muscle mass gain, …)
exercise sessions

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Production ML
Take the data from Cassandra (written there by the Spark
jobs) and:
• Split into training and test datasets
• Fit models for various sensor types
• Save model parameters
• Evaluate the newly fitted models, and re-evaluate old
data

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Production ML
• We are using convolutional network
• 2 seconds of sensor data input (e.g. a @ 50 Hz for accelerometer; a, g @ 50 Hz for
accelerometer + gyroscope; u, l @ 10 Hz for smart clothes)
• The exercise classes as the outputs
• The training program
• CNN in neon
• Loads the mini–batches from Cassandra
• Fits the model; evaluates the fitted model
• Saves the model parameters into Cassandra
• The re–evaluation program
• Re–evaluates past n models against the latest training dataset; computing accuracy,
precision, recall, f1

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Having code is jolly good

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Running it
• Simplicity
• Ease of orchestration
• Ease of development
• Support for polyglot frameworks and components
• Cost effective resource utilisation

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Docker
• Deploy reliably & consistently
• Execution is fast and light weight
• Simplicity
• Developer friendly workflow
• Fantastic community

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Dockerize Cassandra Dev Environment
• Super low memory settings in cassandra-env.sh
• MAX_HEAP_SIZE=“128M”
• HEAP_NEWSIZE=“24M”
• Remove caches in dev mode in cassandra.yml
• key_cache_size_in_mb: 0
• reduce_cache_sizes_at: 0
• reduce_cache_capacity_to: 0

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Dockerize Cassandra Production
• Use host networking (—net=host) for better network
performance
• Put data, commitlog and saved_caches in volume
mount folders to the underlying host
• Run cassandra on the foreground using (-f)
• Tune JVM heap for optimal size
• Tune JVM garbage collector for your workload

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Mesos
• Distributed systems kernel
• Scales to 10,000s of nodes
• Depends on Zookeeper for fault tolerance and high
availability
• Creates a highly available, scalable single resource pool
• Automatic failover
• Ease of management
• Simple to operate
• Support for Docker container

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Mesos architecture
image source: https://assets.digitalocean.com/articles/mesosphere/mesos_architecture.png

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Cassandra on Mesos
• Running Cassandra as Docker containers
• Custom Dockerfile and entry-point script to control Cassandra configuration
• Marathon to initialize and control

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Cost effective resource in AWS
• Embrace AWS spot instances
• About 50-60% cheaper than on demand instances
• Can be reclaimed without notice if outbidded
• Run dev and staging on spot instances
• Run Spark jobs on spot instances

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