In recent years, population aging and the consequent higher incidence of noncommunicable diseases, have increased the need for long-term health monitoring. Moreover, as healthcare cost is projected to grow substantially by 2030 in OECD countries, the demand for portable, easy-to-use and low cost ultra-low power means of monitoring, diagnosis and prevention rises. Wearable sensors technology for remote health and wellness monitoring is an optimal candidate to tackle this problem and has advanced drastically in the last years. However, wearable sensors impose several design constraints. They must process data in real-time and provide highly accurate diagnosis and adapt to different cases. At the same time, to perform long-term monitoring, they must maximize battery lifetime, hence, usability. However, the need for more accurate algorithms and the need to obtain energy-efficient implementations can work against each other. For this reason, enhancing the energy-accuracy trade-off is essential. Several works in the literature have addressed the energy-accuracy trade-off problem. The general approach is to first develop offline methodologies that maximize the algorithm's accuracy, using signal processing and machine learning. Then, these methods are optimized to be implemented as online designs in resource-constrained ultra-low power platforms. However, different problems can occur in these two steps. Most methods use highly variable datasets (mainly having different subjects), others use fixed parameters tailored to specific conditions, which overall decreases the robustness of the algorithm. In traditional single-core devices, some optimizations whose goal is to lower the algorithms' complexity and computational burden, such as downsampling and features reduction, lead to a loss in precision. With the advances of ultra-low power platforms and new machine learning strategies, even more challenges arise. However, these advances allow to exploit the growing capabilities of the platforms and use innovative and more complex strategies that achieve high levels of accuracy, robustness and energy-efficiency. In this thesis, I propose a set of adaptive strategies in the context of remote health and wellness monitoring for an enhanced energy-accuracy trade-off in wearable sensors. First, I present three methodologies for multi-biosignal monitoring and pathology detection, which adapt to the specific physiological conditions by means of personalization to the subject and knowledge acquired from the signal. Second, in the context of modern heterogeneous wearable platforms, I propose a modular approach to software parallelization and hardware acceleration for biomedical applications to maximize the attainable speed-up and, therefore, minimizing energy consumption. Moreover, I propose an approach to scale computing resources and independent memory banks based on the specific characteristics of the patient in modern wearable sensors. Finally, in the context of intensive physical exercise, I propose an online design that adapts to the sudden physiological changes occurring in the signal. This method combines a lightweight algorithm with a more robust though more complex one to reduce energy consumption while maintaining a very high accuracy. Moreover, this adaptive strategy exploits the heterogeneity of modern platforms by matching the complexity of each algorithm with the capabilities of each core, which further enhances the energy-accuracy trade-off.