Deep Learning-Based Human Activity Recognition for Continuous Activity and Gesture Monitoring for Schizophrenia Patients With Negative Symptoms

Abstract

Background: We aimed to develop a Human Activity Recognition (HAR) model using a wrist-worn device to assess patient activity in relation to negative symptoms of schizophrenia. Methods: Data were analyzed in a randomized, three-way cross-over, proof-of-mechanism study (ClinicalTrials.gov: NCT02824055) comparing two doses of RG7203 with placebo, given as adjunct to stable antipsychotic treatment in patients with chronic schizophrenia and moderate levels of negative symptoms. Baseline negative symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS) and Brief Negative Symptom Scale (BNSS). Patients were given a GeneActiv ™ wrist-worn actigraphy device to wear over a 15week period. For this analysis, actigraphy data and behavioral and clinical assessments obtained during placebo treatment were used. Motivated behavior was evaluated with a computerized effort-choice task. A trained HAR model was used to classify activity and an activity-time ratio was derived. Gesture events and features were inferred from the HARdetected activities and the acceleration signal.

Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

“…This moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices is referred to as "digital phenotyping" (13,14). There is now a growing body of research demonstrating that digital phenotyping data may enable the identification of people suffering from or at risk of developing mental disorders, in some cases even before symptoms are visible (or detectable) using traditional methods (11,(15)(16)(17)(18).…”

Remote monitoring of progression in early Parkinson’s disease: reliability and validity of the Roche PD Mobile Application v2

“…This same feature has been related to decreased expressivity in patients with schizophrenia with negative symptoms 38 . Here, gesture power was specifically related to the MDS-UPDRS bradykinesia subscore and item scores, as well as the rigidity subscore, and is in line with a slowing of hand movement in daily non-gait-related activities such as gesturing when speaking, eating, etc.…”

“…Here, sensor data segments during gesture movements were identified from the circa 90% non-walking periods in the passive monitoring sensor data stream, using the squared magnitude of the accelerometer sensor movement as the sensor feature. This same feature has been related to decreased expressivity in patients with schizophrenia with negative symptoms 38 . Here, gesture power was specifically related to the MDS-UPDRS bradykinesia subscore and item scores, as well as the rigidity subscore, and is in line with a slowing of hand movement in daily non-gait-related activities such as gesturing when speaking, eating, etc.…”

Effectiveness and cost-effectiveness of a web-based routine assessment with integrated recommendations for action for depression and anxiety (RehaCAT+): protocol for a cluster randomised controlled trial for patients with elevated depressive symptoms in rehabilitation facilities

“…62 63 First studies show promising results highlighting the potential of this method to complement PROM assessments for monitoring and predicting symptoms with minimal added patient burden. [64][65][66][67][68][69][70] In future the combination of high quality PROM at fixed timepoints combined with continuous monitoring through smart sensing and information from the clinical information system could become a promising data base, which could be used to (1) predict symptom trajectories, (2) build early-detection of adverse events systems (RED-flag) or (3) personalised treatment recommendation systems. [71][72][73][74] Hence, this study additionally investigates the extent to which smart sensing is suitable for assessing mental health in a routine care setting.…”