Activity recognition, as an important component of behavioral monitoring and intervention, has attracted enormous attention, especially in Mobile Cloud Computing (MCC) and Remote Health Monitoring (RHM) paradigms. While recently resource constrained wearable devices have been gaining popularity, their battery life is limited and constrained by the frequent wireless transmission of data to more computationally powerful back-ends. This paper proposes an ultra-low power activity recognition system using a novel adaptive compressed sensing technique that aims to minimize transmission costs. Coarse-grained on-body sensor localization and unsupervised clustering modules are devised to autonomously reconfigure the compressed sensing module for further power saving. We perform a thorough heuristic optimization using Grammatical Evolution (GE) to ensure minimal computation overhead of the proposed methodology. Our evaluation on a real-world dataset and a low power wearable sensing node demonstrates that our approach can reduce the energy consumption of the wireless data transmission up to 81.2% and 61.5%, with up to 60.6% and 35.0% overall power savings in comparison with baseline and a naive state-of-the-art approaches, respectively. These solutions lead to an average activity recognition accuracy of 89.0% only 4.8% less than the baseline accuracy-while having a negligible energy overhead of on-node computation.

Background and purpose: “Brain fog” is a frequent and disabling symptom that can occur after SARS‐CoV‐2 infection. However, its clinical characteristics and the relationships among brain fog and objective cognitive function, fatigue, and neuropsychiatric symptoms (depression, anxiety) are still unclear. In this study, we aimed to examine the characteristics of brain fog and to understand how fatigue, cognitive performance, and neuropsychiatric symptoms and the mutual relationships among these variables influence subjective cognitive complaints.Methods: A total of 170 patients with cognitive complaints in the context of post‐COVID syndrome were evaluated using a comprehensive neuropsychological protocol. The FLEI scale was used to characterize subjective cognitive complaints. Correlation analysis, regression machine‐learning algorithms, and mediation analysis were calculated.Results: Cognitive complaints were mainly attention and episodic memory symptoms, while executive functions (planning) issues were less often reported. The FLEI scale, a mental ability questionnaire, showed high correlations with a fatigue scale and moderate correlations with the Stroop test, and anxiety and depressive symptoms. Random forest algorithms showed an R² value of 0.409 for the prediction of FLEI score, with several cognitive tests, fatigue and depression being the best variables used in the prediction. Mediation analysis showed that fatigue was the main mediator between objective and subjective cognition, while the effect of depression was indirect and mediated through fatigue.Conclusions: Brain fog associated with COVID‐19 is mainly characterized by attention and episodic memory, and fatigue, which is the main mediator between objective and subjective cognition. Our findings contribute to understanding the pathophysiology of brain fog and emphasize the need to unravel the main mechanisms underlying brain fog, considering several aspects.

Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives.

Purpose: Premonitory symptoms (PSs) of migraine are those that precede pain in a migraine attack. Previous studies suggest that treatment during this phase may prevent the onset of pain; however, this approach requires that patients be able to recognize their PSs. Our objectives were to evaluate patients’ actual ability to predict migraine attacks based on their PSs and analyze whether good predictors meet any characteristic profile. Patients and methods: This prospective, observational study included patients with migraine with and without aura. Patients’ baseline characteristics were recorded. During a 2-month follow-up period, patients used a mobile application to record what they believed to be PSs and later to record the onset of pain, if this occurred. When a migraine attack ended, patients had to complete a form on the characteristics of the episode (including the presence of PSs not identified prior to the attack). Results: Fifty patients were initially selected. A final total of 34 patients were analyzed, recording 229 attacks. Of whom, 158 (69%) were accompanied by PSs and were recorded prior to the pain onset in 63 (27.5%) cases. A total of 67.6% of the patients were able to predict at least one attack, but only 35.3% were good predictors (>50% of attacks). There were only 11 cases in which a patient erroneously reported their PSs (positive predictive value: 85.1%). Good predictors were not differentiated by any specific clinical characteristic. However, a range of symptoms were particularly predictive; these included photophobia, drowsiness, yawning, increased thirst, and blurred vision. Conclusion: A large majority of patients with migraine experienced a PS and were able to predict at least one attack. Besides, only a small percentage of patients were considered as good predictors; however, they could not be characterized by any specific profile. Nonetheless, when patients with migraine believed that they were experiencing PSs, they were frequently correct.