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Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common mental disorders affecting children. Because the etiology of ADHD is complex and its symptoms are not specific, there is a lack of feasible quantitative diagnostic methods. Pursuing objective and non-invasive detection methods and standards is of great practical significance to prevent the development of the disease. In this study, we aim to address one specific concern about the objectivity and quantification of ADHD diagnosis. Over a year, we iteratively designed and tested WeDA, a scale-driven wearable diagnostic assessment system. This system contains an Android computer machine with a large touchscreen, a suite of 3D printed interactive devices, and six wearable motion sensors. We implement ten diagnostic tasks drawing on the symptoms of ADHD based on DSM-5. The experimental results of classifying children with ADHD and typically developing children and subjective evaluations from doctors, parents, and children validate the effectiveness and acceptability of WeDA.
Spiral drawing has been utilized for years as a clinical tool to observe tremors and other abnormal movements in the assessment of different movement disorders. Specifically, in Parkinson's Disease (PD), patients' motor functionalities are measured by various tests, and spiral drawing is one of the proven techniques for assessing the severity of PD motor symptoms. Traditionally, this test is performed on pen and paper, and visually assessed by a clinician. There have been successful efforts for digitizing this test on tablets. Here, we describe a smartphone-based digitized version of the spiral drawing test. Moreover, we introduce a square-shaped drawing to solve an identified challenge of a smaller screen estate: finger occlusion while drawing. Both approaches are evaluated with 8 Parkinson's Disease patients and 6 age-matching control participants. Based on earlier studies and our data, we select suitable motion parameters for quantifying the task. Our results show an observable, statistically difference in performance between users with Parkinson's Disease and the control group in drawing accuracy.
Fatigue is a common debilitating symptom of many autoimmune diseases, including multiple sclerosis. It negatively impacts patients' every-day life and productivity. Despite its prevalence, fatigue is still poorly understood. Its subjective nature makes quantification challenging and it is mainly assessed by questionnaires, which capture the magnitude of fatigue insufficiently. Motor fatigability, the objective decline of performance during a motor task, is an underrated aspect in this regard. Currently, motor fatigability is assessed using a handgrip dynamometer. This approach has been proven valid and accurate but requires special equipment and trained personnel. We propose a technique to objectively quantify motor fatigability using a commodity smartphone. The method comprises a simple exertion task requiring rapid alternating tapping. Our study with 20 multiple sclerosis patients and 35 healthy participants showed a correlation of rho = 0.8 with the baseline handgrip method. This smartphone-based approach is a first step towards ubiquitous, more frequent, and remote monitoring of fatigability and disease progression.
Obstructive pulmonary diseases cause limited airflow from the lung and severely affect patients' quality of life. Wheeze is one of the most prominent symptoms for them. High requirements imposed by traditional diagnosis methods make regular monitoring of pulmonary obstruction challenging, which hinders the opportunity of early intervention and prevention of significant exacerbation. In this work, we explore the feasibility of developing a mobile sensor-based system as a convenient means of assessing the severity of pulmonary obstruction via respiration phase-based symptomatic wheeze sensing. We conduct a 131 subjects' (91 patients and 40 healthy) study for the detection (F1: 87.96%) and characterization (F1: 79.47%) of wheeze. Subsequently, we develop novel wheeze metrics, which show a significant correlation (Pearson's correlation: -0.22, p-value: 0.024) with standard spirometry measure of pulmonary obstruction severity. This work takes a principal step towards the unobtrusive assessment of pulmonary condition from mobile sensor interactions.
Due to a lack of medical resources or oral health awareness, oral diseases are often left unexamined and untreated, affecting a large population worldwide. With the advent of low-cost, sensor-equipped smartphones, mobile apps offer a promising possibility for promoting oral health. However, to the best of our knowledge, no mobile health (mHealth) solutions can directly support a user to self-examine their oral health condition. This paper presents OralCam, the first interactive app that enables end-users' self-examination of five common oral conditions (diseases or early disease signals) by taking smartphone photos of one's oral cavity. OralCam allows a user to annotate additional information (e.g. living habits, pain, and bleeding) to augment the input image, and presents the output hierarchically, probabilistically and with visual explanations to help a laymen user understand examination results. Developed on our in-house dataset that consists of 3,182 oral photos annotated by dental experts, our deep learning based framework achieved an average detection sensitivity of 0.787 over five conditions with high localization accuracy. In a week-long in-the-wild user study (N=18), most participants had no trouble using OralCam and interpreting the examination results. Two expert interviews further validate the feasibility of OralCam for promoting users' awareness of oral health.