Project ARM



=Problem Definition=

Individuals recovering from post-stroke arm impairments spend significantly more time conducting therapeutic exercise at home without therapist supervision than they spend doing supervised therapy in a clinical setting. It is difficult to quantify the amount and quality of arm usage that takes place between supervised therapy sessions, which would be useful to know both as an indicator of functional recovery, and also as a way to monitor activity level and its impact on motor recovery.

=General Goal=

The goal of the project is to develop an android application that will monitor and track mobility parameters of arm movements, including range of motion and repetitions of prescribed movements. Update the wearable interface to protect components and allow the users to easily use the wearable interface.

=Project Specific Goals and Target Specifications=
 * Goal 1: Develop the wearable hardware to secure sensors to user
 * (high priority) Secure attachment to the user (minimal risk for misalignment with long-term use during ADL tasks)
 * (high priority) Easy to put on and take off (preferably with one hand)
 * Comfortable fit to user
 * Low profile
 * (low priority) Protect and strain-relief cabling (wired/wireless?)
 * (low priority) Protect electronics from damage (collisions, moisture)
 * Goal 2: Measure arm movement signals from the shoulder and elbow.
 * (high priority) shoulder elevation, horizontal abduction/extension, and internal/external rotation (must take into account torso orientation)
 * elbow flexion/extension
 * Goal 3: Store and compute meaningful metrics for remote and periodic activity monitoring
 * (high priority) range of motion
 * high priority) repetitions of range of motion exercises


 * Goal 4: Count occurrences (repetitions) of pre-recorded movements in the data (in real-time or in post-processing)
 * (high priority) Record two movements, and compute a % difference or % similarity score between them
 * (high priority) Locate and count number of recorded movements from a data set with movements having a desired range of % difference (or similarity).
 * Integrate the movement recognition and repetition counting process in the wearable prototype
 * (low priority) extend to allow multiple (up to 4) different pre-recorded movements that will be identified and counted


 * Goal 5: Display activity information to a screen
 * (high priority) range of motion
 * (high priority) number of repetitions of a desired movement range
 * Movement smoothness
 * Repetitions of a desired movement trajectory
 * PC and mobile device compatible
 * (low priority) real-time activity information

=Project Learning Examples=

Arm Movement Illustration


The human arm has five different types of arm movements. Three for the shoulder: flexion/extension, abduction/adduction, and medial/lateral rotation. Two for the forearm: flexion/extension and supination/pronation.

Possible Algorithm Illustration


This algorithm was used for an EU funded project which utilized two sensors to track arm movements, one on the upper arm and one on the wrist. Data from the sensors is used to calculate the angle of shoulder flexion/extension, elbow flexion/extension, and the z position vector of the forearm which are then used to identify three different movements. The three movements are: reaching for and retrieving an object, lifting an object to the mouth, and rotating an object.

=Design Work Examples=

Hardware Case




Phone App
The intent of the application design was to create a very simple user interface for a Physical Therapist and his/her Patient.


 * The Doctor will be able to assign tasks, track progress, and watch the patient improve in real-time. This will allow for a unique exercise plan for every patient, every day.


 * The Patient will, similarly, be able to see exact exercises along with the amount of repetitions recommended by the Doctor, track his/her progress, and comment on any pain points/successes/questions that he/she may have during these exercises.

Data Collection



 * Data will be used in the assessment of how well a patient performed an exercise.
 * It will be collected from three different sensors in hardware.
 * Shoulder Sensor
 * Wrist Sensor
 * Reference Sensor
 * Data collection shall occur in cycles, with each sensor broadcasting its data at a time relative to the other sensors.




 * ‘Number Lines’ will be used in the analysis of the collected data
 * Example: If there are thirty (30) data points cycles recorded by the hardware, and ten (10) points on the ‘Number Line,’ then each point on the ‘Number Line’ will contain the average of three (3) of those thirty (30) collected data points.
 * Example: (23.54 + 27.82 + 31.91) / 3 = 27.76
 * There shall be two different ‘Number Lines’ used for each axis of each sensor, meaning that a total of eighteen (18) ‘Number Lines’ will be used.
 * Baseline ‘Number Line’
 * This ‘Number Line’ will consist of pre-recorded values to which the values of the ‘collected values’ number line will be compared
 * Collected Value ‘Number Lines’
 * This ‘Number Line’ will consist of averaged data points that will be compared against those contained on the Baseline ‘Number Lines.’

=Final Product Examples=

Hardware Case


3D printed prototype of Assembly Design 1 (Lid Design 1, Base Design 1)

Casing concepts are created with the following parameters in mind:


 * Provides adequate protection for the electrical circuits


 * Provides stabilization for circuit boards to receive accurate data,


 * Can be applied to the brace design.



This case is able to be attached to the “posture” brace via the loops.


 * The adjustment straps go through the loops on the case, providing support and stabilization.

The holes on the corners eliminate the need to pre-drill holes for screw installation.

The inside of the case is able to house the three electrical boards along with a lithium battery.

Future corrections to this basic design could include:


 * The ability to stabilize the boards via “columns” that would contain pre-drilled holes to easily screw the boards to the case.

Phone App
These are screenshots of the current version of the app. Future updates will:


 * Implement data collection and evaluation from the hardware.


 * Implement password reset functionality to the Doctor portion of the app.


 * Add videos for Patients and Doctors to watch to review exercises.


 * Enable 'Help' and 'My Notes' features.

=Team Members=