Energy-efficient Wearable-to-Mobile Offload of ML Inference for
  PPG-based Heart-Rate Estimation
Alessio Burrello Matteo Risso Noemi Tomasello Yukai Chen Luca Benini Enrico Macii Massimo Poncino Daniele Jahier Pagliari
Modern smartwatches often include photoplethysmographic (PPG) sensors to
measure heartbeats or blood pressure through complex algorithms that fuse PPG
data with other signals. In this work, we propose a collaborative inference
approach that uses both a smartwatch and a connected smartphone to maximize the
performance of heart rate (HR) tracking while also maximizing the smartwatch's
battery life. In particular, we first analyze the trade-offs between running
on-device HR tracking or offloading the work to the mobile. Then, thanks to an
additional step to evaluate the difficulty of the upcoming HR prediction, we
demonstrate that we can smartly manage the workload between smartwatch and
smartphone, maintaining a low mean absolute error (MAE) while reducing energy
consumption. We benchmark our approach on a custom smartwatch prototype,
including the STM32WB55 MCU and Bluetooth Low-Energy (BLE) communication, and a
Raspberry Pi3 as a proxy for the smartphone. With our Collaborative Heart Rate
Inference System (CHRIS), we obtain a set of Pareto-optimal configurations
demonstrating the same MAE as State-of-Art (SoA) algorithms while consuming
less energy. For instance, we can achieve approximately the same MAE of
TimePPG-Small (5.54 BPM MAE vs. 5.60 BPM MAE) while reducing the energy by
2.03x, with a configuration that offloads 80\% of the predictions to the phone.
Furthermore, accepting a performance degradation to 7.16 BPM of MAE, we can
achieve an energy consumption of 179 uJ per prediction, 3.03x less than running
TimePPG-Small on the smartwatch, and 1.82x less than streaming all the input
data to the phone.