  Wrist-worn smart devices are providing increased insights into human health,
behaviour and performance through sophisticated analytics. However, battery
life, device cost and sensor performance in the face of movement-related
artefact present challenges which must be further addressed to see effective
applications and wider adoption through commoditisation of the technology. We
address these challenges by demonstrating, through using a simple optical
measurement, photoplethysmography (PPG) used conventionally for heart rate
detection in wrist-worn sensors, that we can provide improved heart rate and
human activity recognition (HAR) simultaneously at low sample rates, without an
inertial measurement unit. This simplifies hardware design and reduces costs
and power budgets. We apply two deep learning pipelines, one for human activity
recognition and one for heart rate estimation. HAR is achieved through the
application of a visual classification approach, capable of robust performance
at low sample rates. Here, transfer learning is leveraged to retrain a
convolutional neural network (CNN) to distinguish characteristics of the PPG
during different human activities. For heart rate estimation we use a CNN
adopted for regression which maps noisy optical signals to heart rate
estimates. In both cases, comparisons are made with leading conventional
approaches. Our results demonstrate a low sampling frequency can achieve good
performance without significant degradation of accuracy. 5 Hz and 10 Hz were
shown to have 80.2% and 83.0% classification accuracy for HAR respectively.
These same sampling frequencies also yielded a robust heart rate estimation
which was comparative with that achieved at the more energy-intensive rate of
256 Hz.
