Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery

por Adans-Dester, Catherine, Hankov, Nicolas, O’Brien, Anne, Vergara-Diaz, Gloria, Black-Schaffer, Randie, Zafonte, Ross, Dy, Jennifer, Lee, Sunghoon I., Bonato, Paolo
Publicado 2020

Predicting and Monitoring Upper-Limb Rehabilitation Outcomes Using Clinical and Wearable Sensor Data in Brain Injury Survivors

por Lee, Sunghoon I., Adans-Dester, Catherine P., O’Brien, Anne T., Vergara-Diaz, Gloria P., Black-Schaffer, Randie, Zafonte, Ross, Dy, Jennifer G., Bonato, Paolo
Publicado 2021

Identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology

por Lee, Sunghoon I., Campion, Andrew, Huang, Alex, Park, Eunjeong, Garst, Jordan H., Jahanforouz, Nima, Espinal, Marie, Siero, Tiffany, Pollack, Sophie, Afridi, Marwa, Daneshvar, Meelod, Ghias, Saif, Sarrafzadeh, Majid, Lu, Daniel C.
Publicado 2017

Using Intervention Mapping and Behavior Change Techniques to Develop a Digital Intervention for Self-Management in Stroke: Development Study

por Wong, Alex W K, Fong, Mandy W M, Munsell, Elizabeth G S, Metts, Christopher L, Lee, Sunghoon I, Nicol, Ginger E, DePaul, Olivia, Tomazin, Stephanie E, Kaufman, Katherine J, Mohr, David C
Publicado 2023

Utilization of a novel digital measurement tool for quantitative assessment of upper extremity motor dexterity: a controlled pilot study

por Getachew, Ruth, Lee, Sunghoon I, Kimball, Jon A, Yew, Andrew Y, Lu, Derek S, Li, Charles H, Garst, Jordan H, Ghalehsari, Nima, Paak, Brian H, Razaghy, Mehrdad, Espinal, Marie, Ostowari, Arsha, Ghavamrezaii, Amir A, Pourtaheri, Sahar, Wu, Irene, Sarrafzadeh, Majid, Lu, Daniel C
Publicado 2014