Depth Sensor-Based In-Home Daily Activity Recognition and Assessment System for Stroke Rehabilitation
| Autor: | Zoe Moore, Rachel Proffitt, Carter Sifferman, Mengxuan Ma, Marjorie Skubic, Shaniah Tullis |
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| Rok vydání: | 2019 |
| Předmět: |
medicine.medical_specialty
Rehabilitation Computer science medicine.medical_treatment Key recovery 020207 software engineering 02 engineering and technology Limiting medicine.disease Data set Activity recognition Physical medicine and rehabilitation Data quality 0202 electrical engineering electronic engineering information engineering medicine Action recognition 020201 artificial intelligence & image processing Stroke |
| Zdroj: | BIBM |
| Popis: | Stroke is a leading cause of long-term adult disability. Many stroke patients participate in rehabilitation programs prescribed by an occupational therapist to aid in recovery; however, occupational therapists rely on in-clinic assessments and often-unreliable self-assessments at home to track a patient's progress, limiting their ability to monitor how patients perform outside of a clinical setting. Our Daily Activity Recognition and Assessment System collects depth and skeletal data passively from within the patient's home to assess long-term recovery and provide metrics to an occupational therapist to allow for more individualized rehabilitation plans. Using data from a wall-mounted depth sensor, we adapt a hierarchical co-occurrence network to identify actions from pre-segmented skeletal data. We then perform assessments on the classified actions to track key recovery metrics: normalized jerk, speed of motions, and extent of reach. We also introduce novel filters to identify high quality data for analysis. Our sensor was installed in a stroke patient's kitchen for seven days, generating the first action recognition data set from a stroke patient in a naturalistic environment. We use this data in conjunction with the NTU-RGB-D data set to validate our recognition and assessment algorithms. We achieved 90.1% accuracy by replicating the results of the NTU-RGB-D data set and a maximum of 59.6% accuracy on our kitchen data set. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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