Assisted Rehabilitation Care During Post-stroke mANaGement: fEasibiLity Assessment

The ARCANGEL study evaluates the feasibility of introducing ARC (Assisted Rehabilitation Care), a new device for home-based post-stroke rehabilitation in the current clinical practise. All the stroke survivors included in the study will received their own equipment to be used at home for 6 months.

Some relevant studies have indicated that approximately 36% of these survivors (i.e. more than 9 million in 2013 only) are left with significant disabilities 5 years after their stroke, and \>40% (i.e. more than 10 million) require assistance with activities of daily living.

Despite evidence that participation in formal rehabilitative therapies lessens disability after stroke, less than a third receive inpatient or outpatient therapies. Of those who do access therapies, the frequency of use varies by geographic location and socioeconomic status. In this context, the development of new strategies able to expand the access to rehabilitation to an increased number of stroke patients, also enabling home-based conduction and monitoring, are increasingly necessary both for patients, their families and for the healthcare and social services sustainability. Since many barriers could limit access to continuous physical rehabilitation for these patients, devices that complement or assist in the rehabilitation process can be of great help.

Among different approaches proposed by the scientific community, technological systems based on accelerometers seem to be among the most promising. Accelerometers are small low cost electronic devices, able to measure body parts acceleration on three axes. Many researchers have already highlighted that accelerometers have the capability to provide reliable and objective information on quantity and intensity of patient limbs movements during recovery process.

Wearable devices such as accelerometers allow to monitor exercises and daily activities. Machine learning methodologies have already been applied for modelling and contextualizing accelerometric signals to identify activity types (walking, dressing, eating, washing up, etc.) or to recognize to which rehabilitative exercise these signals are linked to. These techniques allow to estimate the recorded movement quality, providing information useful to identify the context in which movements are performed. Results of these type of studies are promising and they demonstrate that machine learning is a preferred approach for accelerometric data analysis, since able to exceed actual limits that today are hampering commercial product development for real time analysis of movement.

Within this scenario, Camlin-ARC takes its place. ARC is a platform based on wearable inertial sensors and machine learning algorithms, designed to bring the rehabilitation at post-stroke patients' home, following hospital discharge.

Eligibility Criteria:
Inclusion Criteria:

* Stroke Diagnosis, with a stable clinical condition
* Age \> 18
* Modified Rankin score lower or equal to 4 or Barthel Index score greater than 10 at the time of enrollment
* Patients must be able to keep the standing position without or with minimum assistance
* Patient giving written consent and engage

Exclusion Criteria:

* Significant cognitive impairment and behavioral disorders - judged by a responsible clinician
* Poor communication or reading skills - judged by a Speech and Language Therapist
* Orthopedic limitation (fractures, amputations, advance osteoarthritis, active rheumatoid arthritis)
* Head trauma
* Epilepsy, not pharmacologically controlled
* Severe spatial neglect
* Neurodegenerative and neuromuscular diseases
* Severe spasticity
* Patient not giving written consent and not engage