Personalized Models and Health Maintenance for mobility

Personalized Models and Health Maintenance for mobility Fregly and Rodgers

Overview • Personalized Models • Health Maintenance • Gaps • Summary

Overview • Personalized Models • Motivation • Personalized modeling methods • Full-body scan for personalized model creation • Customization of robot function using personalized models • Health Maintenance • Gaps • Summary

Why Personalized Models? • “One size fits none” - everyone is different! • Increases objectivity in treatment planning (different clinicians may plan different treatments given same patient data). • Can facilitate identification of previously unknown treatments (e.g., modified gait to treat knee OA). • May permit identification of best treatment option for a specific patient. • May permit identification of sensitive treatment parameters (i.e., which parameter values do clinicians need to “get right”?)

Highly Variable Outcomes For some treatments, standard deviation in outcome is bigger than the effect size. Where problems are too complex for models Where models should be focused Scope of model applicability must be properly defined.

HIGH FIDELITY ANATOMIC SHOULDER & Elbow MODEL Frans C.T. van der Helm, Ph.D., Delft

Delft Shoulder and Elbow Model • High fidelity anatomic musculoskeletal model constructed from extensive measurements performed on a single cadaver specimen. • Model accounts for more variables (including sarcomere length) than any other upper extremity model. • Model validation: Muscle forces cannot be measured, so no strict validation! • The same model personalization approach cannot be performed on living patients.

Model Applications • Glenohumeralarthrodesis • Glenohumeralendoprosthesis • Tendon transfer after brachial plexus lesion • “Reverse” shoulder endoprosthesis • Scapula fractures • Functional electrical stimulation for tetraplegics • Neurological disorders • Computer Assisted Surgical Planning (CASP) • Wheelchair propulsion • Garbage collection • Brick-layering

ORTHOPAEDIC SURGERY AND REHABILITATION Maria Benedetti, M.D., Alberto Leardini, Ph.D., and Marco Viceconti, Ph.D. - Bologna

Possible Uses of Gait Analysis • Assessment – Assess after treatment how the treatment worked for a group of patients. (Common) • Identification – Identify on an individual patient basis which patients should be treated (but not how they should be treated). (Becoming more common) • Prediction – Predict on an individual patient basis which treatment should be performed and how it should be performed (where personalized models may help). (Does not yet happen) • The potential value of Prediction depends on the clinical problem at hand.

Clinical Example for Prediction • Clinical Situation:Oncological patients who receive a limb salvage procedure. • Problem: How to get the bone allograft to heal – it needs load to repair but not so much that it breaks. • Observation: Each case is unique – surgical and rehabilitation design are not stereotypical. • Proposed solution: Treatment design using gait and imaging data in a personalized musculoskeletal model that estimates muscle & bone loads. • Challenge: How to gain confidence in patient-specific predictions of muscle & bone loads?

Personalized Model Creation & Use Valente et al., Computer Aided Medicine Conference, 2010

Design of Total Ankle Replacement Leardini et al., ClinOrthopRelat Res, 2004 Though not personalized, design developed using patient data and modeling methods.

PATIENT-SPECIFIC MUSCULOSKELETAL MODELS Bart Koopman, Ph.D. and Herman van der Kooij, Ph.D., Enschede

Model Personalization • Problem: Most musculoskeletal models are generic, and uniform scaling is inaccurate. • Solution: Scale/deform a generic parametric model to match each patient. • Image based scaling of bone geometry (CT, MRI) • Functional kinematic scaling of joint positions/orientations (marker-based motion, laser scans, inertial sensors) • Functional dynamic scaling of muscle strength (dynamometers) • Challenge: Fusion of data from different modalities.

Model Utilization • Collect pre-treatment imaging, kinematic, and dynamic data. • Simulate surgical scenarios and parameters. • Select scenario and parameters that optimize post-treatment outcome. • Implement plan in surgical navigation system. • Validate model predictions using surgical cases not planned with model. • Example: Which tendon to transfer to restore hip abduction strength in patients with Trendelenburg gait?

Neuro-Mechanical Models • How do people interact with and adapt to their environments (e.g., with robotic systems)? • Visual, proprioceptive, and vestibular feedback all play a role. • No simulation tools currently exist to optimize human-machine interactions in rehabilitation devices.

3d PERSONALIZED MUSCULOSKELETAL MODELS WaffaSkalli, Ph.D., Paris

Research Goal Multiscale personalized human musculoskeletal models that enable: • Early detection of balance abnormalities. • Design of innovative devices for prevention and treatment of musculoskeletal disorders. • Identification of the source of pathology (e.g., is it muscular or skeletal?). • Quantitative assessment of treatment strategies.

Personalized Modeling Personalized spine models for studying scoliosis Partners: Hospitals in Paris, Saint Etienne, and Montreal

Biplane X-ray Modeling Technology

Internal-External Registration Where are the bones with respect to the skin markers? Bi-plane X-rays with External Markers Gait Data Direct registration of presonalizedskeletalmodels to external marker locations for gaitanalysis.

Hocoma - Advanced functional movement therapy Peter Hostettler, PhD & CEO, and team, Zurich

Future Directions • Neurorehabilitation is current focus. • Orthopaedic rehabilitation viewed as a potentially big future market. • Current robotic training system designed using the gait pattern of one of the designers. • Customization of robot to individual patients could be valuable in the the future (with possible role of personalized personalized modeling).

Overview • Personalized Models • Health Maintenance • Remote monitoring • Remote training & treatment • Prediction modeling • Gaps • Summary

Remote monitoring and Remotely supervised training & treatment HermieHermens, PhD, Enschede

Remote Health Care Vision • Goal: Create new health care services by combining biomedical engineering with information and communication technology. • “Enabling monitoring and treatment of subjects anywhere, anytime and intervene when needed.” • Remote monitoring – Remote measurement of vital biosignals without interfering with daily activities. • Remotely supervised training & treatment – monitoring + feedback that enable a patient to train when and where convenient and with the same quality of training as in a clinical environment.

Benefits • Remote Monitoring • Less intramural care (costs) • More freedom for patient • Peace of mind • Remotely Supervised Treatment • High intensity training possible (more = better) • Training in natural environment translates to more effective training • Puts patient in driver seat • Clinician can ‘treat’ several patients at the same time • Main challenges are technological feasibility and clinical/patient acceptance.

Example: Tele-Treatment of Chronic Back Pain • Studies report a change in activity level due to chronic back pain. • Clinical study: 29 chronic back pain patients and 20 asymptomatic controls • Activity levels monitored for 7 consecutive days using an 3D inertial motion sensor • Overall activity levels the same but activity patterns different between groups. • Will normalization of activity patterns through feedback improve outcome? (Clinical trial running)

Example: Tele-Treatment of Neck/Shoulder Pain • Chronic shoulder/neck pain typically shows no clear physiological overloading. • Solution: Design a remote feedback system to warn patients when insufficient relaxation occurs. • Muscle relaxation assessed via surface EMG with real-time feedback provided to patient and therapist. • 100 patients treated in Belgium, Germany, Sweden, and the Netherlands • Outcome as good as classic treatment • Approach appreciated by patients and therapists

INSTITUT FOR SUNDHEDSVIDENSKAB OG TEKNOLOGIAalborg Universityaalborg, DenmarK DEPT. OF HEALTH SCIENCE AND TECHNOLOGY

TeleKat COPD (KOL) TeleKat projectapplies User Driven Innovation to develop wireless telehomecare technology enabling COPD patients to perform self-monitoring of their status, and to maintain rehabilitation activities in their homes.

Technology to monitor older adults • Systems deployed to 620 people • Building Predictive models based on data collected Brian Caulfield, Academic Director

Gait Analysis Platform (GAP) TRIL Gait Analysis Platform (GAP) consists of:• Pressure sensing walkway (Tactex, S4 Sensors, Victoria, BC, Canada)• Two SHIMMER™ kinematic sensors worn on the subject’s shanks• Two orthogonally mounted web cameras Unobtrusive capture of gait parameters and physiological data in 600 patients. Data used develop diagnostics capabilities to detect increased gait variability & unsteadiness in elderly people (Predicting fall risk to 85% accuracy). Can help with early identification of onset of diseases such as Parkinson’s

Wellness and Exercise • A complete home/work technology platform has been developed for the project, using a wearable wireless sensors system (SHIMMERs™) and an open shareable software platform (BioMOBIUS™). • This facilitates effective monitoring and biofeedback during exercise whilst enhancing end-user motivation and involvement in the process.

Balance & Strength Exercise • Balance and Strength Exercise (BaSE) program includes console-based system installed in each of the participant’s homes. • System guides the user through each of their exercises, reminding them of correct way to execute each movement. • System prompts participant to carry out prescribed number of exercise repetitions. • Using a combination of camera and kinematic sensors, BaSE system provides real-time feedback to participant on their performance and transmits data collected to the physiotherapist. • Allows monitoring and modification of prescribed exercise programs between clinic visits.

Gaps for Personalized Modeling • How to validate model predictions (especially for internal quantities such as muscle, joint, and bone loads)? • How to calibrate “unobservable” parameters to which model predictions are sensitive?. • How to create personalized neural control models? • How to make generation of model-based predictions fast and easy for a clinical setting?

Gaps for Health Maintenance • User-centered development • Effective technology transfer • Demonstration of efficacy • Need for models to identify predictive variables

Summary for Personalized Modeling • We have lots of technology! What we need are better ways to predict how to use technology to achieve significant improvements in mobility for specific patients and impairments. • Personalized modeling is one option for predicting how to use technology more effectively. • Creating personalized musculoskeletal models is not enough – we also need to include personalized neural control/neuroplasticity models so that patient responses to possible treatments can be predicted.

Summary for Health Maintenance • Technology for monitoring in progress • Collaborations world-wide • Need for user-centered development • Predictive models needed

Personalized Models and Health Maintenance for mobility