Alex Mihailidis

A Survey on Ambient-Assisted Living Tools for Older Adults

In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges.

The use of computer vision in an intelligent environment to support aging-in-place, safety, and independence in the home

This paper discusses the use of computer vision in pervasive healthcare systems, specifically in the design of a sensing agent for an intelligent environment that assists older adults with dementia during an activity of daily living. An overview of the techniques applied in this particular example is provided, along with results from preliminary trials completed using the new sensing agent. A discussion of the results obtained to date is presented, including technical and social issues that remain for the advancement and acceptance of this type of technology within pervasive healthcare.

Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process

This paper presents a real-time vision-based system to assist a person with dementia wash their hands. The system uses only video inputs, and assistance is given as either verbal or visual prompts, or through the enlistment of a human caregivers help. The system combines a Bayesian sequential estimation framework for tracking hands and towel, with a decision-theoretic framework for computing policies of action. The decision making system is a partially observable Markov decision process, or POMDP. Decision policies dictating system actions are computed in the POMDP using a point-based approximate solution technique. The tracking and decision making systems are coupled using a heuristic method for temporally segmenting the input video stream based on the continuity of the belief state. A key element of the system is the ability to estimate and adapt to user psychological states, such as awareness and responsiveness. We evaluate the system in three ways. First, we evaluate the hand-tracking system by comparing its outputs to manual annotations and to a simple hand-detection method. Second, we test the POMDP solution methods in simulation, and show that our policies have higher expected return than five other heuristic methods. Third, we report results from a ten-week trial with seven persons moderate-to-severe dementia in a long-term care facility in Toronto, Canada. The subjects washed their hands once a day, with assistance given by our automated system, or by a human caregiver, in alternating two-week periods. We give two detailed case study analyses of the system working during trials, and then show agreement between the system and independent human raters of the same trials.

The use of an intelligent prompting system for people with dementia

perform routine activities: They cannot remember the proper sequence of steps or how to use the necessary tools. Strategies commonly used by caregivers involve continually providing reminders or cues. Family caregivers find assisting their loved ones to be particularly upsetting and embarrassing, as it necessitates invasion of privacy and role reversal. This difficult situation often results in the family caregiver not being able to cope, and the affected person being placed in a care facility. In response to the unique needs of older adults with dementia, we have been developing a new prompting device that uses artificial intelligence (AI) to automatically monitor an older adult during a common self-care activity (i.e., hand washing) and provide prompts as needed.

The development of an adaptive upper-limb stroke rehabilitation robotic system

BackgroundStroke is the primary cause of adult disability. To support this large population in recovery, robotic technologies are being developed to assist in the delivery of rehabilitation. This paper presents an automated system for a rehabilitation robotic device that guides stroke patients through an upper-limb reaching task. The system uses a decision theoretic model (a partially observable Markov decision process, or POMDP) as its primary engine for decision making. The POMDP allows the system to automatically modify exercise parameters to account for the specific needs and abilities of different individuals, and to use these parameters to take appropriate decisions about stroke rehabilitation exercises.MethodsThe performance of the system was evaluated by comparing the decisions made by the system with those of a human therapist. A single patient participant was paired up with a therapist participant for the duration of the study, for a total of six sessions. Each session was an hour long and occurred three times a week for two weeks. During each session, three steps were followed: (A) after the system made a decision, the therapist either agreed or disagreed with the decision made; (B) the researcher had the device execute the decision made by the therapist; (C) the patient then performed the reaching exercise. These parts were repeated in the order of A-B-C until the end of the session. Qualitative and quantitative question were asked at the end of each session and at the completion of the study for both participants.ResultsOverall, the therapist agreed with the system decisions approximately 65% of the time. In general, the therapist thought the system decisions were believable and could envision this system being used in both a clinical and home setting. The patient was satisfied with the system and would use this system as his/her primary method of rehabilitation.ConclusionsThe data collected in this study can only be used to provide insight into the performance of the system since the sample size was limited. The next stage for this project is to test the system with a larger sample size to obtain significant results.

Automated detection of unusual events on stairs

This paper presents a method for automatically detecting unusual human events on stairs from video data. The motivation is to provide a tool for biomedical researchers to rapidly find the events of interest within large quantities of video data. Our system identifies potential sequences containing anomalies, and reduces the amount of data that needs to be searched by a human. We compute two sets of features from a video of a person descending a stairwell. The first set of features are the foot positions and velocities. We track both feet using a mixed state particle filter with an appearance model based on histograms of oriented gradients. We compute expected (most likely) foot positions given the state of the filter at each frame. The second set of features are the parameters of the mean optical flow over a foreground region. Our final classification system inputs these two sets of features into a hidden Markov model (HMM) to analyse the spatio-temporal progression of the stair descent. A single HMM is trained on sequences of normal stair use, and a threshold on sequence likelihoods is used to detect unusual events in new data. We demonstrate our system on a data set with five people descending a set of stairs in a laboratory environment. We show how our system can successfully detect nearly all anomalous events, with a low false positive rate. We discuss limitations and suggest improvements to the system.

A haptic-robotic platform for upper-limb reaching stroke therapy: Preliminary design and evaluation results

BackgroundIt has been shown that intense training can significantly improve post-stroke upper-limb functionality. However, opportunities for stroke survivors to practice rehabilitation exercises can be limited because of the finite availability of therapists and equipment. This paper presents a haptic-enabled exercise platform intended to assist therapists and moderate-level stroke survivors perform upper-limb reaching motion therapy. This work extends on existing knowledge by presenting: 1) an anthropometrically-inspired design that maximizes elbow and shoulder range of motions during exercise; 2) an unobtrusive upper body postural sensing system; and 3) a vibratory elbow stimulation device to encourage muscle movement.MethodsA multi-disciplinary team of professionals were involved in identifying the rehabilitation needs of stroke survivors incorporating these into a prototype device. The prototype system consisted of an exercise device, postural sensors, and a elbow stimulation to encourage the reaching movement. Eight experienced physical and occupational therapists participated in a pilot study exploring the usability of the prototype. Each therapist attended two sessions of one hour each to test and evaluate the proposed system. Feedback about the device was obtained through an administered questionnaire and combined with quantitative data.ResultsSeven of the nine questions regarding the haptic exercise device scored higher than 3.0 (somewhat good) out of 4.0 (good). The postural sensors detected 93 of 96 (97%) therapist-simulated abnormal postures and correctly ignored 90 of 96 (94%) of normal postures. The elbow stimulation device had a score lower than 2.5 (neutral) for all aspects that were surveyed, however the therapists felt the rehabilitation system was sufficient for use without the elbow stimulation device.ConclusionAll eight therapists felt the exercise platform could be a good tool to use in upper-limb rehabilitation as the prototype was considered to be generally well designed and capable of delivering reaching task therapy. The next stage of this project is to proceed to clinical trials with stroke patients.

An Intelligent Powered Wheelchair to Enable Mobility of Cognitively Impaired Older Adults: An Anticollision System

Older adults with cognitive impairments are generally prohibited from using powered wheelchairs, because of the high risk of collisions with people and objects. This paper describes and presents the preliminary results of a system that uses an infrared sensor to provide anticollision and a prompting system for a powered wheelchair that helps guide the user safely past obstacles. Trials with the prototyped system detected collisions and stopped the chair in 95% of trials with an object and generated no false alarms

Towards a single sensor passive solution for automated fall detection

Falling in the home is one of the major challenges to independent living among older adults. The associated costs, coupled with a rapidly growing elderly population, are placing a burden on healthcare systems worldwide that will swiftly become unbearable. To facilitate expeditious emergency care, we have developed an artificially intelligent camera-based system that automatically detects if a person within the field-of-view has fallen. The system addresses concerns raised in earlier work and the requirements of a widely deployable in-home solution. The presented prototype utilizes a consumer-grade camera modified with a wide-angle lens. Machine learning techniques applied to carefully engineered features allow the system to classify falls at high accuracy while maintaining invariance to lighting, environment and the presence of multiple moving objects. This paper describes the system, outlines the algorithms used and presents empirical validation of its effectiveness.

Reducing fall risk by improving balance control: Development, evaluation and knowledge-translation of new approaches

PROBLEM Falling is a leading cause of serious injury, loss of independence, and nursing-home admission in older adults. Impaired balance control is a major contributing factor. METHODS Results from our balance-control studies have been applied in the development of new and improved interventions and assessment tools. Initiatives to facilitate knowledge-translation of this work include setting up a new network of balance clinics, a research-user network and a research-user advisory board. RESULTS Our findings support the efficacy of the developed balance-training methods, balance-enhancing footwear, neuro-prosthesis, walker design, handrail-cueing system, and handrail-design recommendations in improving specific aspects of balance control. IMPACT ON KNOWLEDGE USERS: A new balance-assessment tool has been implemented in the first new balance clinic, a new balance-enhancing insole is available through pharmacies and other commercial outlets, and handrail design recommendations have been incorporated into 10 Canadian and American building codes. Work in progress is expected to have further impact.