Article by Cheng Chen
LEOMO 5 Sensor Run Form Analysis System Review
In more ways than not, I am a traditionalist. I tend to shun away from the latest technological eye candy promising to revolutionize performance. However, when a product has proven clear advantages, I’ll adopt it without a second thought. This was my mindset when I first heard of LEOMO; and, thus far, I’m beyond the verge of wholehearted adoption.
LEOMO is a 5-sensor motion analysis toolkit meant to help the runner detect and ultimately improve on form deficiencies. Here, I specifically mean toolkit because the approach is not like traditional sensors that an athlete might buy (to own). Instead, this is sold as a service where the sensors are sent to you whereby you then return it upon completion of an analytical workout.
This approach will allow LEOMO to eventually maintain an accessible price point of around $100 for each analysis as opposed to the significantly higher cost of physically owning the full suite of devices. Across time, runners can perform multiple instances of analysis, tracking and improving their form based on snapshots of progress.
The entire package is efficiently packed into a single container with easy to ship-back instructions.
Presently during the initial roll-out phase, LEOMO is being offered for $449-799 as a purchase of its entire suite of sensors. While the runner is the ultimate end-user, current customers tend to be more running coaches, clubs, labs, and professionals who seek to invest in and directly own this platform. I received LEOMO, ran the tests, and returned the unit. Read on to see what I discovered.
Sent in a nicely packed parcel, the service comes with 5 sensors: 2x for the feet, 2x for the quads, and 1x for the sacral back. On top of this, there is an Android device for data capture and transmission as well as various fixtures to hold down the sensors. The runner then carefully wears all these devices along with a belt holding the data capture gadget. Tights are recommended for their ability to further cover and hold down the quadricep and sacral sensors.
Note that the sensors are stuck onto the skin via double-sided tape, hence the need for tights.
With this setup in place, the athlete is instructed to start recording from the main device across a progressive warm-up followed by a half pyramid workout. The workout spans paces from easy to threshold and beyond, allowing for a full capture of form data across all the speeds of a runner.
Throughout the workout, data is recorded live via the Android device, which the runner then stops and saves upon conclusion of the workout. And because LEOMO comes with LTE connectivity, the session can be directly uploaded for analysis via the app, no WiFi needed! However, while the overall process is fairly streamlined, there were intricacies in its execution that proved troublesome, which I’ll discuss in the recommendations section below.
I performed my analysis in tangent with Stryd data, verifying the higher-level data.
Upon upload of the workout data, LEOMO will spend around a week to produce a form analysis report along with a graphed output of the data. Currently, 12 data variables are accessed from the raw sensor data. These variables are called Motion Performance Indicators (MPI), with many being similar to those of simpler tools like Stryd. The 12 MPIs and their descriptions are as follows with “official LEOMO definitions in quotes.”
Strike Angular Range
“Change in angle of the shin from maximal forward extension until initial ground contact” - the greater the angle demonstrates more dorsiflexion of the foot, preparing you to strike the ground and quickly transitioning into your stride.
“The percentage of ground contact time spend absorbing vertical impact forces before transitioning to horizontal propulsion” - the smaller this proportion, the more efficiently you transition upon striking the ground
Recoil Angular Range (RAR)
Change angle of the shin from toe-off to the maximally raised height - this is the proverbial “back kick” representing the ability both to strongly toe-off and efficiently cycle your legs into the next stride.
Thigh Swing Speed (TSS)
“The maximum angular velocity of the thighs as it swings forward (out from the back kick)” - this is correlated with RAR in that the greater the former, the more aggressively you might swing your legs forward into the next stride.
“The measurable length of lateral hip movement” - the lower this score the less side-to-side movement and thus better efficiency.
“The magnitude of hip rotation around the spine” - this correlates with big, open strides and hence decreases in value when fatigued.
“Indicates the amount of up and down tilt of the waist” - a smaller score indicates a better use of the pelvic and gluteal muscles, eliminating unnecessary vertical shake.
“Measured as a change of angle from initial ground contact to flat stance” - this indicates heel/midfoot/forefoot stride patterns.
Ground Contact Time
“The duration of foot contact on ground” - a shorter period generally indicates a more elastically efficient stride but is also correlated directly with speed.
“The total length of vertical oscillation” - this combined with the lateral smoothness score paints a coherent picture of overall form smoothness.
“Total step count per minute” - although high is not necessarily better, changes in cadence across pace can indicate overall stride patterns such as a runner that depends more on opening up stride length as opposed to increasing cadence for faster running.
“Measurement of the angle between the ground and lateral edge of the foot upon ground contact” - this indicates the overall degree of pronation that the foot might traverse assuming an eventual flat stance.
Once received by LEOMO, an analyst then assesses your form by analyzing it across all the pace ranges of the half pyramid workout. This is done by extracting insight from the various MPIs, which are further divided into left/right legs when appropriate. This approach provides a holistic picture of a runner’s form and imbalances as both speed increases and the body tires.
For me, the feedback concurred with an observation I’ve held regarding my form: that my right leg is weaker than the left’s and that a lack in angular range of motion inhibits a large, strong stride.
Right is a screenshot of my analysis at marathon pace; note the left/right differences. Left panel is where my results score for key metrics.
This is demonstrated by both a longer ground contact time and lower Recoil AR of the right foot when compared to the left’s at marathon pace. This delta points to the possibility that my right foot is not striking the ground with as much force and elasticity as my left foot is. And it’s true - I’m consciously aware of a slight compensation given the added pain and stress felt across my right lower kinetic chain. Luckily LEOMO’s report includes drills and recommendations on how to address this form deficiency.
As a courtesy to RTR readers, I’ve included my entire range of MPI data; analyze away!
Conclusion and Recommendations
LEOMO is unique. It bridges a gap that forebears like RunScribe and entrenched players like Stryd lack: functional insights via human analysis. The result is a report that not only details actionable data, but also a professional interpretation followed by recommendations. There are, however, a few recommendations that I have for the produce (note that these are from Fall 2021):
While the instructions on applying the sensors and performing the workout were very clear, the portion on actually using the Android app was confusing. This can either be remedied with notational screenshots or an improved UI/UX.
Consider deploying a non-trouchscreen data capture device. In attempting to save and upload the data after the workout run, my sweaty fingers could barely operate a somewhat alien UI - I luckily did not accidentally delete my data.
Overall, I’m impressed by LEOMO’s, both by the true value of their insights and by their daringness to attack a notoriously difficult market.
Functionality: 10/10 (50%) - “LEOMO legitimately does what it claims and more, zero gimmicks!”
Ease-of-Use: 8/10 (25%) - “The overall end-user experience could be further refined.”
Value: 9/10 (25%) - “Most runners can benefit from form analysis and improvement; they just don’t realize it.”
Cheng is a CrossFitter turned runner. He lifts and base builds in the winter while racing in the summer with personal bests of 5:29 (Mile), 1:20 (Half), and 17:53 (5K). He passionately brings an engineering stance to analyzing running, shoes, and tech. Follow him on Instagram (@MrChengChen) for more.
Tested samples were provided at no charge for review purposes. RoadTrail Run has affiliate partnerships and may earn commission on products purchased through affiliate links in this article. These partnerships do not influence our editorial content. The opinions herein are entirely the authors'
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