How Wearable Sensing Can Be Used to Monitor Patient Recovery Post ACL Reconstruction; The Impact of Gender Inequality in Clinical Research for Anterior Cruciate Ligament Tears

Over 100,000 patients in the United States annually elect to have Anterior Cruciate Ligament reconstruction (ACLR) in hopes of returning to pre-injury level of activity. In the first two years following an ACLR, patients are at their highest risk for re-injury to both the repaired and contralateral knee. With the use of wearable ubiquitous sensors, the technical project monitors patients post-ACLR and how they can be used to aid medical decision-making regarding rehabilitation progressions and reduce injury rates. The tightly coupled sociotechnical project, using the Actor Network framework, investigates the unintended consequences brought about by the historical gender inequities in clinical trials. It specifically highlights the lack of knowledge surrounding causes of female ACL tears. The use of these wearable sensors allows doctors to look more closely at ACL rehabilitation. Also allowing them to investigate more closely female ACL tears and female specific rehabilitation.
The technical project looks at leveraging sensing technologies to monitor patients post ACLR and investigate how body sensors can be used to aid medical decision-making regarding rehabilitation progressions. The overall incidence rate of an ACLR patient having to go through a second repair in 24 months is six times greater than someone who has never had an ACL tear. Early detection of functional deficits is vital to optimize post-operative rehabilitation and to restore normal movement patterns in patients, especially in those who are young with continued risk exposure from competitive sports. Current rehabilitation methods require unconventional movements which cannot be done in the early stages of recovery in fear of damaging the newly repaired ligament.
In our Institutional Review Board (IRB) approved study, patient data, extracted from wearable sensors during several functional assessments, was used for multi-level analysis to extract features indicative of mobility and muscle activation. From this, we have identified key features for determining patient health post-ACLR and implemented these into a machine learning model to predict the accuracy and efficacy of this data as a recovery measurement. Our work is best interpreted as an initial exploration of how Electromyography (EMG) data can be used to assess patient recovery. With the features identified, future researchers can incorporate these findings into bigger solutions to reduce the time, effort,
and inaccuracy associated with assessing patient ACLR recovery.
The relative risk of ACL injury in women is 3 to 8 times greater than males but there has been little research looking into this issue. The question that is what are unintended consequences brought about by the historical gender inequities in clinical trials? Using historical and ethical analysis, the sociotechnical research looked at the effect that the lack of equality has had on women’s health and specifically as it pertains to ACL injuries. It then analyzed impact of gender inequalities within the trials using Wyatt’s Actor Network Theory, looking more in depth at the interaction between the groups and their individual views on solving the issue of gender inequality.
The sociotechnical research highlighted the lack of information pertaining to women’s health and female ACL tears. Using Wyatt’s Actor Network Theory, it was determined that many groups are involved in the solution, and different methods to achieve it. There is no singular solution, but instead multiple different steps that need to be taken. This all starts with greater inclusion of women everywhere in the process.
Ubiquitous sensors for ACL recovery allow for constant data monitoring and more personalized information. More information leads to better decision making and better-informed doctors. Technology and data in the medical field has the ability to change the landscape of ACLR recovery and reduce the ACL injury rate of women.

School of Engineering and Applied Science
Bachelor of Science in Systems Engineering
Technical Advisor: Mehdi Boukhechba
STS Advisor: Rider Foley
STS Advisor: Catherine Baritaud
Technical Team Members: Kevin Cox, Drew Hamrock, Sean Lynch, Jon Saksvig, Jane Romness, Alice Warner