5 research outputs found
Human activity recognition for physical rehabilitation
The recognition of human activity is a challenging topic for machine learning. We present an analysis of Support Vector Machines (SVM) and Random Forests (RF) in their ability to accurately classify Kinect kinematic activities. Twenty participants were captured using the Microsoft Kinect performing ten physical rehabilitation activities. We extracted the kinematic location, velocity and energy of the skeletal joints at each frame of the activity to form a feature vector. Principle Component Analysis (PCA) was applied as a pre-processing step to reduce dimensionality and identify significant features amongst activity classes. SVM and RF are then trained on the PCA feature space to assess classification performance; we undertook an incremental increase in the dataset size.We analyse the classification accuracy, model training and classification time quantitatively at each incremental increase. The experimental results demonstrate that RF outperformed SVM in classification rate for six out of the ten activities. Although SVM has performance advantages in training time, RF would be more suited to real-time activity classification due to its low classification time and high classification accuracy when using eight to ten participants in the training set. © 2013 IEEE
Human Activity Recognition for Physical Rehabilitation
The recognition of human activity is a challenging topic for machine learning. We present an analysis of Support Vector Machines (SVM) and Random Forests (RF) in their ability to accurately classify Kinect kinematic activities. Twenty participants were captured using the Microsoft Kinect performing ten physical rehabilitation activities. We extracted the kinematic location, velocity and energy of the skeletal joints at each frame of the activity to form a feature vector. Principle Component Analysis (PCA) was applied as a pre-processing step to reduce dimensionality and identify significant features amongst activity classes. SVM and RF are then trained on the PCA feature space to assess classification performance; we undertook an incremental increase in the dataset size.We analyse the classification accuracy, model training and classification time quantitatively at each incremental increase. The experimental results demonstrate that RF outperformed SVM in classification rate for six out of the ten activities. Although SVM has performance advantages in training time, RF would be more suited to real-time activity classification due to its low classification time and high classification accuracy when using eight to ten participants in the training set. © 2013 IEEE
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Multilingual markers of depression in remotely collected speech samples: a preliminary analysis
BackgroundSpeech contains neuromuscular, physiological and cognitive components, and so is a potential biomarker of mental disorders. Previous studies indicate that speaking rate and pausing are associated with major depressive disorder (MDD). However, results are inconclusive as many studies are small and underpowered and do not include clinical samples. These studies have also been unilingual and use speech collected in controlled settings. If speech markers are to help understand the onset and progress of MDD, we need to uncover markers that are robust to language and establish the strength of associations in real-world data.MethodsWe collected speech data in 585 participants with a history of MDD in the United Kingdom, Spain, and Netherlands as part of the RADAR-MDD study. Participants recorded their speech via smartphones every two weeks for 18 months. Linear mixed models were used to estimate the strength of specific markers of depression from a set of 28 speech features.ResultsIncreased depressive symptoms were associated with speech rate, articulation rate and intensity of speech elicited from a scripted task. These features had consistently stronger effect sizes than pauses.LimitationsOur findings are derived at the cohort level so may have limited impact on identifying intra-individual speech changes associated with changes in symptom severity. The analysis of features averaged over the entire recording may have underestimated the importance of some features.ConclusionsParticipants with more severe depressive symptoms spoke more slowly and quietly. Our findings are from a real-world, multilingual, clinical dataset so represent a step-change in the usefulness of speech as a digital phenotype of MDD.</p
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Engagement with a remote symptom-tracking platform among participants with major depressive disorder (RADAR-Engage): a randomized controlled trial
Background:Multi-parametric remote measurement technologies (RMTs), comprising smartphones and wearable devices, have the potential to revolutionise understanding of the etiology and trajectory of major depressive disorder (MDD). Engagement with RMTs in MDD research is of the upmost importance for validity of predictive analytical methods and long-term use, and can be conceptualized as both objective engagement (data availability) or subjective engagement (system usability and experiential factors). In-app components provide an opportunity to promote engagement with RMTs while minimizing research team resources. Randomized controlled trials (RCTs) are the gold standard in quantifying the effect of in-app components on engagement with RMTs in those with MDD.Objective:This study aimed to evaluate whether a multi-parametric RMT system with a visual progress report tracking data completion, theoretically informed notifications, and access to research team contact details could promote objective and subjective engagement with remote symptom tracking over and above the system as usual.Methods:A 2-arm, parallel-group RCT (participant-blinded) with 1:1 randomization was conducted with 100 participants with MDD over 12 weeks. Participants in both arms employed the RADAR-base system, involving a smartphone app for weekly symptom assessments and a wearable Fitbit device for continuous passive tracking. Participants in the intervention arm (n=50) also had access to the additional in-app components. The primary outcome was objective engagement with the system, measured as the percentage of weekly questionnaires during follow-up. Secondary outcomes comprised measures of subjective engagement (system engagement, system usability, and emotional self-awareness).Results:Levels of completion of the PHQ-9 task were similar in the control (69.2%) and intervention (68.1%) arms (p-value for difference between arms = 0.83, 95% CI -9.32 to 11.65). Those in the intervention group reported slightly higher user engagement (1.93, 95% CI -1.91 to 5.78), emotional self-awareness (1.13, 95% CI -2.93 to 5.19) and system usability (2.29, 95% CI -5.93 to 10.52) scores at follow-up, however all confidence intervals were wide and included 0.Conclusions:The adapted system did not increase objective or subjective engagement with remote symptom tracking in our research cohort. This study provides important foundations for understanding engagement with RMTs for research, and the methodologies by which this work can be replicated in both community and clinical settings. Clinical Trial: This study was registered as a clinical trial (reference number NCT04972474; https://clinicaltrials.gov/ct2/show/NCT04972474), and published as a protocol (https://doi.org/10.2196/32653).</p
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Remote smartphone-based speech collection: acceptance and barriers in individuals with major depressive disorder
The ease of in-the-wild speech recording using smartphones has sparked considerable interest in the combined application of speech, remote measurement technology (RMT) and advanced analytics as a research and healthcare tool. For this to be realised, the acceptability of remote speech collection to the user must be established, in addition to feasibility from an analytical perspective. To understand the acceptance, facilitators, and barriers of smartphone-based speech recording, we invited 384 individuals with major depressive disorder (MDD) from the Remote Assessment of Disease and Relapse - Central Nervous System (RADAR-CNS) research programme in Spain and the UK to complete a survey on their experiences recording their speech. In this analysis, we demonstrate that study participants were more comfortable completing a scripted speech task than a free speech task. For both speech tasks, we found depression severity and country to be significant predictors of comfort. Not seeing smartphone notifications of the scheduled speech tasks, low mood and forgetfulness were the most commonly reported obstacles to providing speech recordings