An Information-Driven Approach for the Early Health Technology Sustainability Assessment and the Frugal Design of the...

Over the past decade, wearable activity monitoring (WAM) devices have become prevalent to improve quality of life and support the prevention and management of a wide range of health disorders. WAM devices based on the Internet of Medical Things (IoMT) paradigm provide a practical means of tracking physical activity, but their widespread adoption raises sustainability concerns. Meanwhile, health technology assessment and life cycle assessment are typically applied at advanced development stages, when empirical certainty about the final design and operating conditions is available, leaving little room for further improvements.

This exploratory work provides the empirical foundations for an information-driven approach addressing this paradox in the early evaluation and conception of wrist-worn step counters, for which evidence suggests overdimensioned and unsustainable electronic designs. Specifically, we identify optimal resource-performance trade-offs in critical electronic components of frugal smartbands based on the data they collect and the information they preserve for step counting. This approach accounts for uncertainties in device reliability, users' gait speeds, and material and energy consumption in final products.

We conducted a secondary analysis of an accelerometer dataset characterizing wrist motion in healthy individuals walking at different speeds. The original x-, y-, and z-axis signals were progressively downsampled using cubic spline interpolation and discretized to quantify the information preserved, first across sampling frequencies and then with respect to changes in motion velocity. We also preliminarily assessed the viability of the downsampled signals for step detection by estimating percentage errors in peak and valley counts. Based on this, we constructed and evaluated 4 frugal smartband design archetypes, linking energy consumption and sampling frequency for 4 widely used accelerometers and combining essential electronic components, implementing a suboptimal asynchronous first-in-first-out (FIFO) algorithm at different sampling rates. Finally, we evaluated the environmental impact and circularity of these components through a streamlined analysis focused on raw materials.

About 70%-90% of the information is lost in signals downsampled at very low frequencies (2-5 Hz), whereas losses remain below 24% from 20 Hz onward. Substantial information loss also occurs when individuals walk briskly or jog (≥8 km/h), or walk below 8 km/h with sampling frequencies below 7 Hz or above 25 Hz. Step-counting accuracy is expected to be acceptable from approximately 11 Hz onward. Conversely, higher sampling rates rapidly saturate FIFO buffers and increase energy overhead, particularly when implemented in memory-dense components handling both processing and data transfer. Finally, gold and silver in transceivers and microcontrollers contribute substantially to resource depletion, while copper remains relevant for material recovery.

These findings provide preliminary insights into the concurrent assessment and development of frugal WAM devices. This work extends the understanding of step detection under knowledge-constrained conditions and provides mechanisms to reduce uncertainty during early health technology assessment and eco-design.