A Multifunctional Adaptive and Interactive AI system to support people living with stroke, acquired brain or spinal cord injuries: A study protocol

Background: Acquired brain injury and spinal cord injury are leading causes of severe motor disabilities impacting a person's autonomy and social life. Enhancing neurological recovery driven by neurogenesis and neuronal plasticity could represent future solutions; however, at present, recovery of activities employing assistive technologies integrating artificial intelligence is worthy of examining. MAIA (Multifunctional, adaptive, and interactive AI system for Acting in multiple contexts) is a human-centered AI aiming to allow end-users to control assistive devices naturally and efficiently by using continuous bidirectional exchanges among multiple sensorimotor information. Methods: Aimed at exploring the acceptability of MAIA, semi-structured interviews (both individual interviews and focus groups) are used to prompt possible end-users (both patients and caregivers) to express their opinions about expected functionalities, outfits, and the services that MAIA should embed, once developed, to fit end-users needs. Discussion: End-user indications are expected to interest MAIA technical, health-related, and setting components. Moreover, psycho-social issues are expected to align with the technology acceptance model. In particular, they are likely to involve intrinsic motivational and extrinsic social aspects, aspects concerning the usefulness of the MAIA system, and the related ease to use. At last, we expect individual factors to impact MAIA: gender, fragility levels, psychological aspects involved in the mental representation of body image, personal endurance, and tolerance toward AT-related burden might be the aspects end-users rise in evaluating the MAIA project

Improving Harmonic Measurements with Instrument Transformers: a Comparison Among Two Techniques

The measurement of harmonics is essential in modern power systems in order to perform distortion level assessment, disturbances source detection and mitigation, etc. In this context, the role of Instrument Transformers (ITs) is crucial, as they are key elements in every power systems measuring instrument. However, inductive ITs, which are still the most widely used, suffer from both a filtering behavior due to their dynamics and from nonlinear effects due to their iron core. The target of this paper is to deeply analyze the performance of two digital signal processing techniques, recently proposed in literature, aimed at mitigating their nonlinear behavior: they are SINDICOMP and the compensation of harmonic distortion through polynomial modeling in the frequency domain. Their performance in improving the measurement of voltage harmonics are analyzed through numerical simulations, by adopting waveforms that can be typically encountered in power systems during normal operating conditions

Co-designing an interactive artificial intelligent system with post-stroke patients and caregivers to augment the lost abilities and improve their quality of life: a human-centric approach

ObjectivesThe motor disability due to stroke compromises the autonomy of patients and caregivers. To support autonomy and other personal and social needs, trustworthy, multifunctional, adaptive, and interactive assistive devices represent optimal solutions. To fulfill this aim, an artificial intelligence system named MAIA would aim to interpret users’ intentions and translate them into actions performed by assistive devices. Analyzing their perspectives is essential to develop the MAIA system operating in harmony with patients’ and caregivers’ needs as much as possible.MethodsPost-stroke patients and caregivers were interviewed to explore the impact of motor disability on their lives, previous experiences with assistive technologies, opinions, and attitudes about MAIA and their needs. Interview transcripts were analyzed using inductive thematic analysis.ResultsSixteen interviews were conducted with 12 post-stroke patients and four caregivers. Three themes emerged: (1) Needs to be satisfied, (2) MAIA technology acceptance, and (3) Perceived trustfulness. Overall, patients are seeking rehabilitative technology, contrary to caregivers needing assistive technology to help them daily. An easy-to-use and ergonomic technology is preferable. However, a few participants trust a system based on artificial intelligence.ConclusionAn interactive artificial intelligence technology could help post-stroke patients and their caregivers to restore motor autonomy. The insights from participants to develop the system depends on their motor ability and the role of patients or caregiver. Although technology grows exponentially, more efforts are needed to strengthen people’s trust in advanced technology

Neural stem cell transplantation in patients with progressive multiple sclerosis: an open-label, phase 1 study

Innovative pro-regenerative treatment strategies for progressive multiple sclerosis (PMS), combining neuroprotection and immunomodulation, represent an unmet need. Neural precursor cells (NPCs) transplanted in animal models of multiple sclerosis have shown preclinical efficacy by promoting neuroprotection and remyelination by releasing molecules sustaining trophic support and neural plasticity. Here we present the results of STEMS, a prospective, therapeutic exploratory, non-randomized, open-label, single-dose-finding phase 1 clinical trial (NCT03269071, EudraCT 2016-002020-86), performed at San Raffaele Hospital in Milan, Italy, evaluating the feasibility, safety and tolerability of intrathecally transplanted human fetal NPCs (hfNPCs) in 12 patients with PMS (with evidence of disease progression, Expanded Disability Status Scale >= 6.5, age 18-55 years, disease duration 2-20 years, without any alternative approved therapy). The safety primary outcome was reached, with no severe adverse reactions related to hfNPCs at 2-year follow-up, clearly demonstrating that hfNPC therapy in PMS is feasible, safe and tolerable. Exploratory secondary analyses showed a lower rate of brain atrophy in patients receiving the highest dosage of hfNPCs and increased cerebrospinal fluid levels of anti-inflammatory and neuroprotective molecules. Although preliminary, these results support the rationale and value of future clinical studies with the highest dose of hfNPCs in a larger cohort of patients

On-line fault detection technique for voltage transformers

Conventional voltage transformers (CVTs) are still widely used as transducers in power networks, thanks to their high reliability, insulation capability, low drift over time and temperature. Their rugged construction is very often used as justification for skipping periodical tests and calibrations, that require putting them off-line, thus avoiding a time-consuming and expensive procedure. For this reason, in the last years, a growing interest has been addressed towards the study of online diagnostic and calibration procedures. The typical approach is based on the frequency response analysis that permits, under sinusoidal excitation, to detect possible deterioration of the behavior of CVT. Anyway, the real interest is to check the CVT fleet already installed and operating on the grid without requiring their disconnection from the grid. As well known, distribution grid voltage features a non negligible harmonic distortion, which may allow the online evaluation of the frequency response of the transformer, by simply connecting a reference transducer. Unfortunately, being the harmonics much lower than the fundamental, this approach cannot be employed in a straightforward way because of the nonlinear behavior of the CVT. This paper proposes an innovative condition monitoring technique of CVTs based on a simplified Volterra model. This opens the way to a new approach to the on-site characterization of CVTs, exploiting the actual voltage of the grid and thus not requiring its disconnection

Simplified Modeling and Identification of Nonlinear Systems Under Quasi-Sinusoidal Conditions

This paper proposes a simplified Volterra model able to represent the steady-state behavior of nonlinear systems in quasi-sinusoidal conditions. A wide class of nonlinear systems can be modeled using the conventional Volterra approach, but as the order of nonlinearity or the memory length increases, the number of coefficients grows exponentially, thus making the identification of the Volterra model troublesome. By considering a system whose input is a periodic signal containing a main frequency component which is much higher than the others, it is possible to drastically reduce the number of coefficients of its frequency-domain Volterra model without affecting the model accuracy. The proposed technique is particularly suitable to represent the behavior of the electrical devices connected to the ac mains, since they typically operate in quasi-sinusoidal conditions. In particular, its application to voltage and current transducers takes on great importance in the field of instrumentation and measurement, since it allows overcoming their usual characterization. Thanks to the proposed model, dynamics and nonlinearities can be considered simultaneously, while avoiding the complexity usually associated with the conventional Volterra approach. For example, the proposed technique is applied to model a Hammerstein system, which is often employed to represent the behavior of electrical devices, and the results are deeply discussed