IEEE Journal of Selected Topics in Signal Processing

Personalized diagnosis and therapy requires monitoring patient activity using various body sensors. Sensor data generated during personalized exercises or tasks may be too specific or inadequate to be evaluated using supervised methods such as classification. We propose multidimensional motif (MDM) discovery as a means for patient activity monitoring, since such motifs can capture repeating patter... View full abstract»

Wearable motion sensors are widely used to estimate metabolic equivalent of task (MET) values associated with physical activities. However, one major obstacle in widespread adoption of current wearables is that any changes in configuration of the network requires new data collection and re-training of the underlying signal processing algorithms. For any wearable-based MET estimation framework to b... View full abstract»

In this work, we present methods for the personalization of a system for the continuous estimation of pain intensity from bio-physiological channels. We investigate various ways to estimate the similarity of persons and to retrieve the most informative ones using meta-information, personality traits, and machine learning techniques. Given this information, specialized classifiers can be created th... View full abstract»

Enabling accurate and low-cost classification of a range of motion activities is important for numerous applications, ranging from disease treatment and in-community rehabilitation of patients to athlete training. This paper proposes a novel contextual online learning method for activity classification based on data captured by low-cost, body-worn inertial sensors, and smartphones. The proposed me... View full abstract»

With increasing importance given to tele-rehabilitation, there is a growing need for accurate, low-cost, and portable motion capture systems that do not require specialist assessment venues. This paper proposes a novel framework for motion capture using only a single depth camera, which is portable and cost effective compared to most industry-standard optical systems, without compromising on accur... View full abstract»

In this paper, an adaptive filtering technique is proposed to estimate and characterize pathological tremors caused by Parkinson's Disease (PD) and Essential Tremor (ET). The technique is based on the formulation of band-limited multiple Fourier Linear Combiners (BMFLC) and is called Enhanced-BMFLC (E-BMFLC). The effectiveness of the designed filter is statistically evaluated through a clinical st... View full abstract»

Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We... View full abstract»

Rehabilitation robots can aid patients to practice activities of daily living in order to enhance muscle strength and recover motor functions. In this paper, we focus on robot-assisted rehabilitation for co-operative therapy tasks that elicit impedance-based behaviors from the patient. For instance, if the rehabilitation robot is controlled to behave as a self-closing door and if pulling this simu... View full abstract»

In this contribution, we consider the factors that influence the information processing capacity of the person using sensory substitution devices, and the influence of how the translated information, here in audio, impacts performance. First, we review aspects of vision substitution by tactile and audio devices, and then we review key theory in human information processing limitations to devise an... View full abstract»

Brain computer interfaces (BCIs) offer individuals with disabilities an alternative channel of communication and control, hence they have been receiving increasing interest. BCIs can also be useful for healthy individuals in situations limiting their movement or where other computer interaction modalities need to be supplemented. Event-related and steady state visually evoked potentials (SSVEPs) a... View full abstract»

Social interaction is a central component of human experience. The ability to interact with others and communicate effectively within an interactive context is a fundamental necessity for professional success as well as personal fulfillment. Individuals with visual impairment face significant challenges in social communication, which if unmitigated, may lead to lifelong needs for extensive social ... View full abstract»

Persons who have lost their larynx and thus speech functionality need to use a substitution voice to regain speech. The electro-larynx (EL) is a widely used device but is known for its unnatural and monotonic speech quality. Previous research has addressed these problems, but until now no significant improvements could be reported. Moreover, the importance of human-centered computing and co-adapta... View full abstract»

Active and passive mobile sensing has garnered much attention in recent years. In this paper, we focus on chronic pain measurement and management as a case application to exemplify the state of the art. We present a consolidated discussion on the leveraging of various sensing modalities along with modular server-side and on-device architectures required for this task. Modalities included are: acti... View full abstract»