In hospitals and smart nursing homes, ambient-intelligent care rooms are equipped with many sensors. They can monitor environmental and body parameters, and detect wearable devices of patients and nurses. Hence, they continuously produce data streams. This offers the opportunity to collect, integrate and interpret this data in a context-aware manner, with a focus on reactivity and autonomy. However, doing this in real time on huge data streams is a challenging task. In this context, cascading reasoning is an emerging research approach that exploits the trade-off between reasoning complexity and data velocity by constructing a processing hierarchy of reasoners. Therefore, a cascading reasoning framework is proposed in this paper. A generic architecture is presented allowing to create a pipeline of reasoning components hosted locally, in the edge of the network, and in the cloud. The architecture is implemented on a pervasive health use case, where medically diagnosed patients are constantly monitored, and alarming situations can be detected and reacted upon in a context-aware manner. A performance evaluation shows that the total system latency is mostly lower than 5 s, allowing for responsive intervention by a nurse in alarming situations. Using the evaluation results, the benefits of cascading reasoning for healthcare are analyzed.

IoT-based solutions for sport analytics aim to improve performance, coaching and strategic insights. These factors are especially relevant in cycling, where real-time data should be available anytime, anywhere, even in remote areas where there are no infrastructure-based communication technologies (e.g. LTE, Wi-Fi). In this paper, we present an experience report on the use of state-of-the-art IoT technologies in cycling, where a group of cyclists can form a reliable and energy efficient mesh network to collect and process sensor data in real-time, such as heart rate, speed and location. This data is analyzed in real-time to estimate the performance of each rider and derive instantaneous feedback. Our solution is the first to combine a local body area network to gather the sensor data from the cyclist and a 6TiSCH network to form a multi-hop long-range wireless sensor network in order to provide each bicycle with connectivity to the sink (e.g. a moving car following the cyclists). In this work we present a detailed technical description of this solution, describing its requirements, options and technical challenges. In order to assess such a deployment, we present a large publicly available data-set from different real-world cycling scenarios (mountain road cycle racing and cyclo-cross) which characterizes the performance of the approach, demonstrating its feasibility and evidencing its relevance and promising possibilities in a cycling context for providing low-power communication with reliable performance.

Background: The diagnosis of headache disorders relies on the correct classification of individual headache attacks. Currently, this is mainly done by clinicians in a clinical setting, which is dependent on subjective self-reported input from patients. Existing classification apps also rely on self-reported information and lack validation. Therefore, the exploratory mBrain study investigates moving to continuous, semi-autonomous and objective follow-up and classification based on both self-reported and objective physiological and contextual data. Methods: The data collection set-up of the observational, longitudinal mBrain study involved physiological data from the Empatica E4 wearable, data-driven machine learning (ML) algorithms detecting activity, stress and sleep events from the wearables’ data modalities, and a custom-made application to interact with these events and keep a diary of contextual and headache-specific data. A knowledge-based classification system for individual headache attacks was designed, focusing on migraine, cluster headache (CH) and tension-type headache (TTH) attacks, by using the classification criteria of ICHD-3. To show how headache and physiological data can be linked, a basic knowledge-based system for headache trigger detection is presented. Results: In two waves, 14 migraine and 4 CH patients participated (mean duration 22.3 days). 133 headache attacks were registered (98 by migraine, 35 by CH patients). Strictly applying ICHD-3 criteria leads to 8/98 migraine without aura and 0/35 CH classifications. Adapted versions yield 28/98 migraine without aura and 17/35 CH classifications, with 12/18 participants having mostly diagnosis classifications when episodic TTH classifications (57/98 and 32/35) are ignored. Conclusions: Strictly applying the ICHD-3 criteria on individual attacks does not yield good classification results. Adapted versions yield better results, with the mostly classified phenotype (migraine without aura vs. CH) matching the diagnosis for 12/18 patients. The absolute number of migraine without aura and CH classifications is, however, rather low. Example cases can be identified where activity and stress events explain patient-reported headache triggers. Continuous improvement of the data collection protocol, ML algorithms, and headache classification criteria (including the investigation of integrating physiological data), will further improve future headache follow-up, classification and trigger detection. Trial registration This trial was retrospectively registered with number NCT04949204 on 24 June 2021 at www.clinicaltrials.gov.

In healthcare, the aging of the population is resulting in a gradual shift from residential care to home care, requiring reliable follow-up of elderly people by a whole network of care providers. The environment of these patients is increasingly being equipped with different monitoring devices, which allow to obtain insight into the current condition of patient & environment. However, current monitoring platforms that support care providers are centralized and not personalized, reducing performance, scalability, autonomy and privacy. Because the available data is only exposed through custom APIs, profile knowledge cannot be efficiently exchanged, which is required to provide optimal care. Therefore, this paper presents a distributed data-driven platform, built on Semantic Web technologies, that enables the integration of all profile knowledge in order to deliver personalized continuous home care. It provides a distributed smart monitoring service, which allows to locally monitor only the relevant sensors according to the patient’s profile, and infer personalized decisions when analyzing events. Moreover, it provides a medical treatment planning service, which composes treatment plans tailored to each patient, including personalized quality of service parameters allowing a doctor to select the optimal plan. To illustrate how the platform delivers these services, the paper also presents a demonstrator using a realistic home care scenario.