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The SENSACTION-AAL experience (2007-2009)
 

The SENSACTION-AAL experience (2007-2009)

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The SENSACTION-AAL project addressed one of the main problems for older people: motor disabilities.

The SENSACTION-AAL project addressed one of the main problems for older people: motor disabilities.

By Lorenzo Chiari, Carlo Tacconi. DEIS - Università di Bologna

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    The SENSACTION-AAL experience (2007-2009) The SENSACTION-AAL experience (2007-2009) Presentation Transcript

    • LORENZO CHIARI, CARLO TACCONI DEIS – Università di Bologna [email_address] The SENSACTION-AAL experience (2007-2009)
      • AIM: The SENSACTION-AAL project addressed one of the main problems for older people: motor disabilities . The ultimate goals of the project were in fact to:
      • assist older people in maintaining independent mobility in daily life activities , and
      • reduce the consequences of falls and injuries
      • Partners:
      SENSing & ACTion to support mobility in Ambient Assisted Living
    • Rationale / 1
      • It has been demonstrated that physical activity based interventions can improve motor and cognitive functioning and decrease risk of falls in older people, both with and without age-related pathology.
      • Evidence suggests more effect when interventions take place over longer time periods , when interventions are individually tailored , and when interventions also include exercises in the home environment .
    • Rationale / II
      • Hence, relevant information which should not be missed include, e.g.
      • if motor activity is done in daily life conditions
      • which is its intensity profile
      • if prescribed corrective/preventive exercises are correctly taken
      • if , when , and under which circumstances dangerous events (e.g. falls) occur
    • 3 Application Scenarios Local (e.g. home rehabilitation and training, QoL assessment for user-awareness, short-term , real-time , etc.) Remote (e.g. providing awereness of patient state after treatment to caregivers, long term analysis of behaviour, off-line ) Local and Remote (fast reactive detection of dangerous events, alarm dispatching to user and caregivers)
    • System Architecture
    • Major Achievements / I Remote Assistant (Scenario III) Smart Monitor (Scenario II) Virtual Trainer (Scenario I)
    • Major achievements / II
      • We released an ecological, accessible system:
      • 1. providing means to perform customized ,
      • repetitive rehabilitation exercises directly at home via closed-loop bio-feedback therapy. T his reduces patient discomfort and caretaker loads in terms of time and mobility .
      • 2. able to perform a monitoring of mobility during daily life activities . This improves knowledge on quantity and quality of motor activity at home.
      3. that can remotely transmit alarms and raw data in case unrecovered falls are automatically detected. This enhances daily home safety and security of elderly people living on their own and increase knowledge on falls .
    • Users’ perspective
      • 18 older subjects (10 PD; 8 PSP) were involved in a multicenter clinical trial
      • Patients enjoyed the training
      • All patients were able to correctly follow the audio information
      • Some reported they were able to “still hear the feedback at home”
      • They reduced their number of falls
      • Increased awareness and concentration
      • Well suited for different disease severity
    • 1-Clinical validation trials
      • 370+ training sessions in PD & PSP patients; very good adherence
      • Training sessions in the home situation suggest feasibility of tele-rehabilitation
    • 2-Activity monitoring
    • 3-Fall documentation 25 reported falls  19 verified falls in 6 subjects Clear differences emerge between real and simulated falls!
    • Management of falls SensAction-AAL Tele-Care Scenario : Accelerometer data are real-time processed by PDA application. An alarm is sent from PDA to the web server in case of fall to advise caregiver (SMS, e-mail) for starting appropriate actions .
    • Management of falls / II
      • PDA implements Real-Time Fall-detection algorithm to detect unrecovered fall.
      • After detection, alarm is sent to the Web Server using secure connection (SSL) to protect data.
      • Data (EDF+) recorded one minute before and after the fall will also be sent to the Web Server to perform post processing data analysis.
    • Management of falls / III Web server-based management of data and information
    • Lessons learnt
      • ICT technologies may offer novel chances to support the natural ageing process and counteract disability and falls. This can contribute to reduce the enormous pressure of an ageing society on the European healthcare systems.
      • Wearable sensors & systems are transforming the way people, including the aged, interact with their own health, raising their awareness.
      • AAL has the effective potential to enhance autonomy and quality of life of elderly people at home and reduce the need for caretakers and personal nursing services at home. The offer of suitable and well accepted technologies for senior citizens is still limited; nevertheless, if older users are involved in the system design since its earlier stages, they are the best prompters of solutions to face the challenges of their age.
      • Several fall detection solutions are available on the market, but there is a need for a joined coordinated effort to build up a critical mass to get validated results (asking young subjects to simulate falls is not the best way to get to know real falls); there is need of data sharing on falls; as well as a need for an accepted taxonomy; standardization and interoperability are mostly welcome.
    • Acknowledgements Martina Mancini, PhD Laura Rocchi, PhD Elisabetta Farella, PhD (Micrel) Angelo Cappello, PhD Luca Benini, PhD (Micrel) Clemens Becker Jeff Hausdorff Wiebren Zijlstra Carlos Cavero Barca Frantisek Hlavacka Rob van Lummel Laura Vanzago For more info: [email_address] www.sensaction-aal.eu [email_address]
    • Thank you for your attention