Ishan Sain
Cart 0


A system to manage mobility for people with OsteoArthritis.

Lighten uses specialized muscle sensors to monitor daily activities and predict their impact on the condition using a machine learning platform. It also allows patient’s care-giving network to stay informed at all times.



Design a system of connected devices to help patients and their caregiver network to better manage the chronic condition (OsteoArthritis).


Systems Design, UX Design, Product Design


Sketch, Principle, Adobe Creative Suite, Rhino 3D


3 weeks


Jorge Arango




What is OsteoArthritis?

A most common chronic degenerative joint condition of the joints. It can affect any joint, but it occurs most often in knees.
Osteoarthritis damages the slippery tissue that covers the ends of bones in a joint. The slippery tissue (cartilage) as well as the synovial fluid in between the joints start decomposing. This allows bones to rub together. The rubbing causes pain, swelling, and loss of motion of the joint. 

Image Source:  PEMF Therapy Education

Once the joints start degrading, there is no coming back.

Only a normal joint maintains Homeostasis. However, the moment it loses the state of equilibrium it falls into a reinforced feedback loop.

Over time the joint degenerates slowly. Excessive physical activity and wear & tear further speeds up the process of joint degeneration. 


Patient Interview

To get an in-depth understanding of a person managing OsteoArthritis on a daily basis, I reached out the nearest patient for an interview. The interview helped me to understand patient's care-giving network and map her journey throughout a week

Janes Mock Picture.jpg

Jane Robinson

  • 68 years

  • Female

  • Drives occasionally for house chores

  • OsteoArthritis in Knee Joints


care-giving network

Jane mostly ends up self managing her condition

It emerged in her interview that she is usually able to manage her condition by herself. However, when the condition of her knee joints becomes worse she needs her family's help. And, only in the worst situations she would visit her Doctor to seek professional help and treatment.

The diagram shows the relative frequency of help Jane receives from her care-giving network.

The diagram shows the relative frequency of help Jane receives from her care-giving network.


Journey Map

Jane would sometimes overwork herself being forgetful of the need for optimal physical work.

To better understand how she manages her condition and navigates herself through a day, I mapped her general schedule and major activities throughout a week. And, it came up that she sometimes overwork herself which would lead to joint pain, swelling, and further use of medications for pain-relief.



1. The gradual degradation of joints with OsteoArthritis cannot be stopped at all but can be slowed down.

2. Patients when not in discomfort tend to push their limits by engaging in physical activities more than they are supposed to.



How might we signal the patients and the network of care-givers about their excessive physical activity by monitoring and maintaining their mobility?



To address the opportunity I started ideating a system that needs to intervene in patients daily life. I focused on two key aspects  – gathering data through a specialized sensor and communicating actionable information back to the patient.

I iterated through different forms of wearable sensors that would be easily and comfortably attached to different muscles of the body. Secondly, doing quick and dirty prototyping using wireframes helped to establish the core structure of the mobile application. The application would act as a touchpoint to deliver information and facilitate the flow of sensor data to the cloud platform for analysis.

Artboard 1.png
Artboard 2.png

proposed system

Artboard 1 copy.jpg

Lighten Sensor Patch

By iterating different forms of a wearable sensor on paper it emerged that a Band-Aid inspired form is scalable in terms of putting multiple sensors on different muscles of the body. Moreover, leveraging on accustomed form and application of band-aid this ‘smart band-aid’ makes a familiar addition to daily use.

Technically, the sensor patch gathers raw muscle activity and motion data with the help of EMG sensor and accelerometer respectively. That raw data is uploaded to the cloud platform which is responsible for synthesizing it using machine learning to figure out patterns. The patterns help in predicting the pain based on all the previous physical activities done. Finally, alerts and suggestions are sent to the patients and their network of caregivers.


Key Screens


Predictive Analysis

Maintain mobility by knowing how daily activities impact the chronic condition. The application communicates the predictions processed by the cloud platform and further allows to dive deep into your activities.


Multiple Sensors

The system supports managing and keeping track of lighten sensors. Sensors can be placed in different formations depending upon the state of condition. Higher number of sensors allow precise monitoring.


Informed Care-giving Network

The care-giving network is informed of all the patient’s activities. Alerts and suggestions are automatically pushed to the care-givers in times of need.


Wireframes - UI Flow



The value of the hidden data

Through this project, I was able to design by revealing the data invisible to the patient and influencing them for a controlled lifestyle. The data not only enables us to act in the present but may also give insights into future implications.

Systemic Intervention

While the systemic interventions are for a positive impact, however, they might unknowingly affect the other aspects of the bigger system. For example, this project aims to improve patients mobility. But I’m also conscious of the possibility that the form of the wearable sensor patch or the way it attaches to the body may introduce other complications in daily life.