Venet Osmani

Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients

Todays health care is difficult to imagine without the possibility to objectively measure various physiological parameters related to patients symptoms (from temperature through blood pressure to complex tomographic procedures). Psychiatric care remains a notable exception that heavily relies on patient interviews and self-assessment. This is due to the fact that mental illnesses manifest themselves mainly in the way patients behave throughout their daily life and, until recently there were no “behavior measurement devices.” This is now changing with the progress in wearable activity recognition and sensor enabled smartphones. In this paper, we introduce a system, which, based on smartphone-sensing is able to recognize depressive and manic states and detect state changes of patients suffering from bipolar disorder. Drawing upon a real-life dataset of ten patients, recorded over a time period of 12 weeks (in total over 800 days of data tracing 17 state changes) by four different sensing modalities, we could extract features corresponding to all disease-relevant aspects in behavior. Using these features, we gain recognition accuracies of 76% by fusing all sensor modalities and state change detection precision and recall of over 97%. This paper furthermore outlines the applicability of this system in the physician-patient relations in order to facilitate the life and treatment of bipolar patients.

Mobile phones as medical devices in mental disorder treatment: an overview

AbstractnMental disorders can have a significant, negative impact on sufferers’ lives, as well as on their friends and family, healthcare systems and other parts of society. Approximately 25xa0% of all people in Europe and the USA experience a mental disorder at least once in their lifetime. Currently, monitoring mental disorders relies on subjective clinical self-reporting rating scales, which were developed more than 50xa0years ago. In this paper, we discuss how mobile phones can support the treatment of mental disorders by (1) implementing human–computer interfaces to support therapy and (2) collecting relevant data from patients’ daily lives to monitor the current state and development of their mental disorders. Concerning the first point, we review various systems that utilize mobile phones for the treatment of mental disorders. We also evaluate how their core design features and dimensions can be applied in other, similar systems. Concerning the second point, we highlight the feasibility of using mobile phones to collect comprehensive data including voice data, motion and location information. Data mining methods are also reviewed and discussed. Based on the presented studies, we summarize advantages and drawbacks of the most promising mobile phone technologies for detecting mood disorders like depression or bipolar disorder. Finally, we discuss practical implementation details, legal issues and business models for the introduction of mobile phones as medical devices.

Tuning to your position: FM radio based indoor localization with spontaneous recalibration

Position of mobile users has become highly important information in pervasive computing environments. Indoor localization systems based on Wi-Fi signal strength fingerprinting techniques are widely used in office buildings with existing Wi-Fi infrastructure. Our previous work has proposed a solution based on exploitation of FM signal to deal with environments not covered with Wi-Fi signal or environments with only single Wi-Fi access point. However, a general problem of indoor wireless positioning systems pertains to signal degradation due to the environmental factors affecting signal propagation. Therefore, in order to maintain a desirable level of localization accuracy, it becomes necessary to perform periodic calibration of the system, which is either time consuming or requires dedicated equipment and expert knowledge. In this paper, we present a comparison of FM versus Wi-Fi positioning systems and a combination of both systems, exploiting their strengths for indoors positioning. Finally, we address the problem of recalibration by introducing a novel concept of spontaneous recalibration and demonstrate it using the FM localization system.

Human activity recognition in pervasive health-care: Supporting efficient remote collaboration

Technological advancements, including advancements in the medical field have drastically improved our quality of life, thus pushing life expectancy increasingly higher. This has also had the effect of increasing the number of elderly population. More than ever, health-care institutions must now care for a large number of elderly patients, which is one of the contributing factors in the rising health-care costs. Rising costs have prompted hospitals and other health-care institutions to seek various cost-cutting measures in order to remain competitive. One avenue being explored lies in the technological advancements that can make hospital working environments much more efficient. Various communication technologies, mobile computing devices, micro-embedded devices and sensors have the ability to support medical staff efficiency and improve health-care systems. In particular, one promising application of these technologies is towards deducing medical staff activities. Having this continuous knowledge about health-care staff activities can provide medical staff with crucial information of particular patients, interconnect with other supporting applications in a seamless manner (e.g. a doctor diagnosing a patient can automatically be sent the patients lab report from the pathologist), a clear picture of the time utilisation of doctors and nurses and also enable remote virtual collaboration between activities, thus creating a strong base for establishment of an efficient collaborative environment. In this paper, we describe our activity recognition system that in conjunction with our efficiency mechanism has the potential to cut down health-care costs by making the working environments more efficient. Initially, we outline the activity recognition process that has the ability to infer user activities based on the self-organisation of surrounding objects that user may manipulate. We then use the activity recognition information to enhance virtual collaboration in order to improve overall efficiency of tasks within a hospital environment. We have analysed a number of medical staff activities to guide our simulation setup. Our results show an accurate activity recognition process for individual users with respect to their behaviour. At the same time we support remote virtual collaboration through tasks allocation process between doctors and nurses with results showing maximum efficiency within the resource constraints.

Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients

In this paper we demonstrate how smart phone sensors, specifically inertial sensors and GPS traces, can be used as an objective measurement device for aiding psychiatric diagnosis. In a trial with 12 bipolar disorder patients conducted over a total (summed over all patients) of over 1000 days (on average 12 weeks per patient) we have achieved state change detection with a precision/recall of 96%/94% and state recognition accuracy of 80%. The paper describes the data collection, which was conducted as a medical trial in a real life every day environment in a rural area, outlines the recognition methods, and discusses the results.

Monitoring activity of patients with bipolar disorder using smart phones

Mobile computing is changing the landscape of clinical monitoring and self-monitoring. One of the major impacts will be in healthcare, where increase in number of sensing modalities is providing more and more information on the state of overall wellbeing, behaviour and health. There are numerous applications of mobile computing that range from wellbeing applications, such as physical fitness, stress or burnout up to applications that target mental disorders including bipolar disorder. Use of information provided by mobile computing devices can track the state of the subjects and also allow for experience sampling in order to gather subjective information. This paper reports on the results obtained from a medical trial with monitoring of bipolar disorder patients and how the episodes of the diseases correlate to the analysis of the data sampled from mobile phone acting as a monitoring device.

Investigation of indoor localization with ambient FM radio stations

Localization plays an essential role in many ubiquitous computing applications. While the outdoor location-aware services based on GPS are becoming increasingly popular, their proliferation to indoor environments is limited due to the lack of widely available indoor localization systems. The de-facto standard for indoor positioning is based on Wi-Fi and while other localization alternatives exist, they either require expensive hardware or provide a low accuracy. This paper presents an investigation into localization system that leverages signals of broadcasting FM radio stations. The FM stations provide a worldwide coverage, while FM tuners are readily available in many mobile devices. The experimental results show that FM radio can be used for indoor localization, while providing longer battery life than Wi-Fi, making FM an alternative to consider for positioning.

Automatic Stress Detection in Working Environments From Smartphones’ Accelerometer Data: A First Step

Increase in workload across many organizations and consequent increase in occupational stress are negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of selfreporting and variability between and within individuals. With the advent of smartphones, it is now possible to monitor diverse aspects of human behavior, including objectively measured behavior related to psychological state and consequently stress. We have used data from the smartphones built-in accelerometer to detect behavior that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (e.g., in comparison to location, video, or audio recording), and because its low-power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. About 30 subjects from two different organizations were provided with smartphones. The study lasted for eight weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify selfreported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer.

FM radio for indoor localization with spontaneous recalibration

The position of mobile users has become highly important information in pervasive computing environments. Indoor localization systems based on Wi-Fi signal strength fingerprinting techniques are widely used in office buildings with an existing Wi-Fi infrastructure. Our previous work has proposed a solution based on exploitation of a FM signal to deal with environments not covered with Wi-Fi signal or environments with only a single Wi-Fi access point. However, a general problem of indoor wireless positioning systems pertains to signal degradation due to the environmental factors affecting signal propagation. Therefore, in order to maintain a desirable level of localization accuracy, it becomes necessary to perform periodic calibrations of the system, which is either time consuming or requires dedicated equipment and expert knowledge. In this paper, we present a comparison of FM versus Wi-Fi positioning systems and a combination of both systems, exploiting their strengths for indoors positioning. We also address the problem of recalibration by introducing a novel concept of spontaneous recalibration and demonstrate it using the FM localization system. Finally, the results related to device orientation and localization accuracy are discussed.

Analysis of Social Interactions Through Mobile Phones

Equipment of mobile phones with various kinds of sensors is transforming these devices from mere capabilities of voice and internet access to devices capable of sensing a number of phenomena pertaining to their users. In this paper we make use of these capabilities of phones to detect social interactions between people and analyze social context by using embedded sensors found in typical smart phones. Work carried out in this area has typically used dedicated hardware to establish social interactions, and we contend on the suitability of mobile phone, since additional devices that user is not familiar with influence natural user behavior and thus their social interaction patterns. Our work shows that two parameters that can be detected through mobile phone sensing, namely interpersonal distance and relative body orientation, provide a solid basis for inferring social interactions. We describe how these factors are acquired using smart phones and describe our analysis. The experiments demonstrate that we can recognize not only whether a social interaction is taking place, but also the type of social interaction, distinguishing between formal and informal social settings.