  Traditional DL models are complex and resource hungry and thus, care needs to
be taken in designing Internet of (medical) things (IoT, or IoMT) applications
balancing efficiency-complexity trade-off. Recent IoT solutions tend to avoid
using deep-learning methods due to such complexities, and rather classical
filter-based methods are commonly used. We hypothesize that a shallow CNN model
can offer satisfactory level of performance in combination by leveraging other
essential solution-components, such as post-processing that is suitable for
resource constrained environment. In an IoMT application context, QRS-detection
and R-peak localisation from ECG signal as a case study, the complexities of
CNN models and post-processing were varied to identify a set of combinations
suitable for a range of target resource-limited environments. To the best of
our knowledge, finding a deploy-able configuration, by incrementally increasing
the CNN model complexity, as required to match the target's resource capacity,
and leveraging the strength of post-processing, is the first of its kind. The
results show that a shallow 2-layer CNN with a suitable post-processing can
achieve $>$90\% F1-score, and the scores continue to improving for 8-32 layer
CNNs, which can be used to profile target constraint environment. The outcome
shows that it is possible to design an optimal DL solution with known target
performance characteristics and resource (computing capacity, and memory)
constraints.
