Associate Professor of Physical Medicine and Rehabilitation Harvard Medical School Charlestown, Massachusetts
Over the past two decades, advances in the development of wearable and mhealth technologies have enabled the collection of massive amounts of data in the clinic and in the home and community settings. Miniature wearable sensors have been used to gather movement and physiological data. Ambient sensors and wearable cameras have provided contextual information. Smartphones and tablets have been utilized to collect ePRO’s. In parallel, advances in the field of machine learning have made it possible to derive clinically-meaningful information from data collected using wearable and mhealth technologies. These developments are transforming the field of rehabilitation. This lecture will review recent advances in the application of wearable and mhealth technologies to the field of rehabilitation. First, the use of these technologies in the clinic will be reviewed. The presenter will discuss how sensing technology is gradually replacing costly camera-based systems and how combining - by relying on machine learning algorithms - data collected using inexpensive cameras with data collected using sensing technology can lead to highly-accurate kinematic data. It will be shown how relying on wearable and mhealth technologies and on machine learning algorithms, researchers have developed approaches suitable to derive accurate estimates of clinical scores via the analysis of sensor data collected during the performance of functional movements. Examples provided during the lecture will include techniques to derive Wolf Motor Function Test, Fugl-Meyer Assessment, and Functional Ability Scale scores from sensor data. Also, it will be shown how wearable and mhealth technologies can be used to collect kinematic and EMG data and derive data about the motor primitives underlying the generation of movement patterns thus improving our ability to assess motor learning. Then evidence will be presented that these technologies are transforming the way rehabilitation interventions are designed and implemented as they enable tracking individual responses to interventions. Next, the lecture will discuss applications of wearable and mhealth technologies outside of the clinic. The presenter will show how these technologies can facilitate the process of gathering information about participation (as defined by the ICF framework) and about the impact of contextual factors on clinical outcomes. It will be shown how measures capturing amount and quality of use - that traditionally have been collected using the Motor Activity Log scale - can be more reliably gathered using wearable and mhealth technologies. Also discussed will be the integration of sensor data and ePRO’s and show how they can complement each other. Then, challenges that researchers are facing in deriving reliable metrics from data collected in the home and community settings will be reviewed. Examples of approaches aimed to assure that such metrics are accurate and reliable will be presented. Furthermore, the lecture will discuss the role of wearable and mhealth technologies in enabling the implementation of interventions with minimum (in the clinic) or no (in the home) direct supervision by therapists while enabling patient monitoring for safety. Finally, the presenter will discuss how we envision that the field of rehabilitation will evolve in the next decade as wearable and mhealth technologies are integrated in clinical practice.
Abbreviated Description: This lecture will provide a critical review of how wearable and mhealth technologies are transforming the field of rehabilitation. We will show how data collected using these technologies in the clinic and in the home and community settings can be utilized to derive estimates of clinical outcomes that would otherwise require the administration of lengthy assessment procedures. Then, we will discuss how these estimates could be utilized to design and implement patient-specific rehabilitation interventions. Finally, we will discuss how we envision that the field of rehabilitation will leverage data collected using wearable and mhealth technologies to deliver better clinical outcomes.
Upon completion, participants will be able to describe how wearable and mhealth technologies can be utilized to derive estimates of clinical outcome measures and design patient-specific interventions.
Upon completion, participants will have basic knowledge concerning available technologies, including how to choose the wearable and mhealth systems that are most suitable for the problem at hand.
Upon completion, participants will have a basic understanding of the machine learning algorithms utilized to analyze data collected using wearable and mhealth technologies.
Upon completion, participants will be able to describe ways wearable and mhealth technologies will affect clinical practice in the years to come.