Estimation of lower limb joint moments using consumer realistic wearable sensor locations and deep learning - finding the balance between accuracy and consumer viability.

We used raw data from wearable sensors in consumer-realistic locations (replicating watch, arm phone strap, chest strap, etc.) to estimate lower-limb sagittal-plane joint moments during treadmill running and assessed the effect of a reduced number of sensor locations on estimation accuracy. Fifty mixed-ability runners (25 men and 25 women) ran on a treadmill at a range of speeds and gradients. Their data was used to train Long Short-Term Memory (LSTM) models in a supervised fashion. Estimation accuracy was evaluated by comparing model outputs against the criterion signals, calculated from marker-based kinematics and instrumented treadmill kinetics via inverse dynamics. The model that utilised data from all sensor locations achieved the lowest estimation error with a mean relative Root Mean Squared Error (rRMSE) of 12.1%, 9.0%, and 6.7% at the hip, knee, and ankle, respectively. Reducing data input to fewer sensors did not greatly compromise estimation accuracy. For example, a wrist-foot sensor combination only increased estimation error by 0.8% at the hip, and 1.0% at the knee and ankle joints. This work contributes to the development of a field-oriented tool that can provide runners with insight into their joint-level net moment contributions whilst leveraging data from their possible existing wearable sensor locations.