Satisfaction, user experiences, and initial efficacy of a technology-supported self-management intervention (iSMART) to improve post-stroke functioning: a remoted randomized controlled trial.

Background: Self-management interventions empower individuals to manage their chronic conditions and daily life after stroke. However, traditional in-person self-management interventions often face transportation and geographical barriers. Digital interventions may offer a solution to address this gap.

Wearable-Based Kinematic Analysis of Upper-Limb Movements During Daily Activities Could Provide Insights into Stroke Survivors' Motor Ability.

Background: Frequent and objective monitoring of motor recovery progression holds significant importance in stroke rehabilitation. Despite extensive studies on wearable solutions in this context, the focus has been predominantly on evaluating limb activity. This study aims to address this limitation by delving into a novel measure of wrist kinematics more intricately related to patients' motor capacity.

A Digital Intervention to Promote Self-Management Self-Efficacy Among Community-Dwelling Individuals With Stroke: Pilot Randomized Controlled Trial.

Background: Digital interventions provided through smartphones or the internet that are guided by a coach have been proposed as promising solutions to support the self-management of chronic conditions. However, digital intervention for poststroke self-management is limited; we developed the interactive Self-Management Augmented by Rehabilitation Technologies (iSMART) intervention to address this gap.

Objective: This study aimed to examine the feasibility and initial effects of the iSMART intervention to improve self-management self-efficacy in people with stroke.

Understanding voluntary human movement variability through data-driven segmentation and clustering.

Recently, we proposed a novel approach where movements are decomposed into sub-segments, termed movement elements. This approach, to date, provides a robust construct of how the brain may generate simple as well as complex movements. Here, we address the issue of motor variability during voluntary movements by applying an unsupervised clustering algorithm to group movement elements according to their morphological characteristics.

Detection and Assessment of Point-to-Point Movements During Functional Activities Using Deep Learning and Kinematic Analyses of the Stroke-Affected Wrist.

Stoke is a leading cause of long-term disability, including upper-limb hemiparesis. Frequent, unobtrusive assessment of naturalistic motor performance could enable clinicians to better assess rehabilitation effectiveness and monitor patients' recovery trajectories. We therefore propose and validate a two-phase data analytic pipeline to estimate upper-limb impairment based on the naturalistic performance of activities of daily living (ADLs).

Using Intervention Mapping and Behavior Change Techniques to Develop a Digital Intervention for Self-Management in Stroke: Development Study.

Background: Digital therapeutics, such as interventions provided via smartphones or the internet, have been proposed as promising solutions to support self-management in persons with chronic conditions. However, the evidence supporting self-management interventions through technology in stroke is scarce, and the intervention development processes are often not well described, creating challenges in explaining why and how the intervention would work.

Objective: This study describes a specific use case of using intervention mapping (IM) and the taxonomy of behavior change techniques (BCTs) in designing a digital intervention to manage chronic symptoms and support daily life participation in people after stroke.

LASense: Pushing the Limits of Fine-grained Activity Sensing Using Acoustic Signals.

Acoustic signals have been widely adopted in sensing fine-grained human activities, including respiration monitoring, finger tracking, eye blink detection, etc. One major challenge for acoustic sensing is the extremely limited sensing range, which becomes even more severe when sensing fine-grained activities. Different from the prior efforts that adopt multiple microphones and/or advanced deep learning techniques for long sensing range, we propose a system called LASense, which can significantly increase the sensing range for fine-grained human activities using a single pair of speaker and microphone.

Envisioning the use of in-situ arm movement data in stroke rehabilitation: Stroke survivors' and occupational therapists' perspectives.

Background: The key for successful stroke upper-limb rehabilitation includes the personalization of therapeutic interventions based on patients' functional ability and performance level. However, therapists often encounter challenges in supporting personalized rehabilitation due to the lack of information about how stroke survivors use their stroke-affected arm outside the clinic. Wearable technologies have been considered as an effective, objective solution to monitor patients' arm use patterns in their naturalistic environments.

A Kinematic Data-Driven Approach to Differentiate Involuntary Choreic Movements in Individuals With Neurological Conditions.

Objective: The ability to differentiate similar choreic involuntary movements could lay the groundwork for the development of a minimally-invasive screening tool for their etiology and provide in-depth understandings of pathophysiology. As a first step, we investigate kinematic differences between Huntington's disease (HD) chorea and Parkinson's disease (PD) choreic levodopa-induced dyskinesia (LID), which have distinct pathological causes yet share a great kinematic resemblance.

Methods: Twenty subjects with HD and ten subjects with PD stood with both upper limbs in front of them for approximately 60 seconds.

Estimating Ground Reaction Force and Center of Pressure Using Low-Cost Wearable Devices.

Objective: Ambulatory monitoring of ground reaction force (GRF) and center of pressure (CoP) could improve management of health conditions that impair mobility. Insoles instrumented with force-sensitive resistors (FSRs) are an unobtrusive, low-cost, and low-power technology for sampling GRF and CoP in real-world environments. However, FSRs have variable response characteristics that complicate estimation of GRF and CoP.

Decomposition of Reaching Movements Enables Detection and Measurement of Ataxia.

Technologies that enable frequent, objective, and precise measurement of ataxia severity would benefit clinical trials by lowering participation barriers and improving the ability to measure disease state and change. We hypothesized that analyzing characteristics of sub-second movement profiles obtained during a reaching task would be useful for objectively quantifying motor characteristics of ataxia. Participants with ataxia (N=88), participants with parkinsonism (N=44), and healthy controls (N=34) performed a computer tablet version of the finger-to-nose test while wearing inertial sensors on their wrists.

Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery.

The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for "precision rehabilitation". Tracking and predicting outcomes defining the recovery trajectory is key in this context.

Estimating Quality of Reaching Movement Using a Wrist-Worn Inertial Sensor.

Stroke is a major cause of long-term disability. Because patients recovering from stroke often perform differently in clinical settings than in their naturalistic environments, remote monitoring of motor performance is needed to evaluate the true impact of prescribed therapies. Wearable sensors have been considered as a technical solution to this problem, but most existing systems focus on measuring the amount of movement without considering the quality of movement.

Predicting and Monitoring Upper-Limb Rehabilitation Outcomes Using Clinical and Wearable Sensor Data in Brain Injury Survivors.

Objective: Rehabilitation specialists have shown considerable interest for the development of models, based on clinical data, to predict the response to rehabilitation interventions in stroke and traumatic brain injury survivors. However, accurate predictions are difficult to obtain due to the variability in patients' response to rehabilitation interventions. This study aimed to investigate the use of wearable technology in combination with clinical data to predict and monitor the recovery process and assess the responsiveness to treatment on an individual basis.

A Simple Low-Cost Wearable Sensor for Long-Term Ambulatory Monitoring of Knee Joint Kinematics.

Objective: Accurate monitoring of joint kinematics in individuals with neuromuscular and musculoskeletal disorders within ambulatory settings could provide important information about changes in disease status and the effectiveness of rehabilitation programs and/or pharmacological treatments. This paper introduces a reliable, power efficient, and low-cost wearable system designed for the long-term monitoring of joint kinematics in ambulatory settings.

Methods: Seventeen healthy subjects wore a retractable string sensor, fixed to two anchor points on the opposing segments of the knee joint, while walking at three different self-selected speeds.

Estimating Upper-Limb Impairment Level in Stroke Survivors Using Wearable Inertial Sensors and a Minimally-Burdensome Motor Task.

Upper-limb paresis is the most common motor impairment post stroke. Current solutions to automate the assessment of upper-limb impairment impose a number of critical burdens on patients and their caregivers that preclude frequent assessment. In this work, we propose an approach to estimate upper-limb impairment in stroke survivors using two wearable inertial sensors, on the wrist and the sternum, and a minimally-burdensome motor task.

A novel upper-limb function measure derived from finger-worn sensor data collected in a free-living setting.

The use of wrist-worn accelerometers has recently gained tremendous interest among researchers and clinicians as an objective tool to quantify real-world use of the upper limbs during the performance of activities of daily living (ADLs). However, wrist-worn accelerometers have shown a number of limitations that hinder their adoption in the clinic. Among others, the inability of wrist-worn accelerometers to capture hand and finger movements is particularly relevant to monitoring the performance of ADLs.

Finger-Worn Sensors for Accurate Functional Assessment of the Upper Limbs in Real-World Settings.

Remote monitoring of stroke survivors' upper limb performance (stroke-affected vs. unaffected limbs) can provide clinicians with information regarding the true impact of rehabilitation in the real-world settings, which allows opportunities to administer individually tailored therapeutic interventions. In this work, we examine the use of finger-worn accelerometers, which are capable of capturing gross-arm as well as fine-hand movements, in order to quantitatively compare the performance of the upper limbs during goal-directed activities of daily living (ADLs).

Towards the Ambulatory Assessment of Movement Quality in Stroke Survivors using a Wrist-worn Inertial Sensor.

Stroke is a leading cause of long-term disability that may lead to significant functional motor impairments in the upper limb (UL). Wrist-worn inertial sensors have emerged as an objective, minimally-obtrusive tool to monitor UL motor function in the real-world setting, such that rehabilitation interventions can be individually tailored to maximize functional performance. However, current wearable solutions focus on capturing the quantity of movement without considering the quality of movement.

Effectiveness of the RAPAEL Smart Board for Upper Limb Therapy in Stroke Survivors: A Pilot Controlled Trial.

We aim to assess the effectiveness of using the RAPAEL Smart Board as an assistive tool for therapists in clinical rehabilitation therapy settings and to investigate if it can be used to improve the motor recovery rate of stroke survivors. The RAPAEL Smart Board is a therapy tool where therapists actively engage patients, giving necessary verbal and physical interventions as in traditional treatment sessions. We conducted a randomized controlled study with 17 stroke survivors.

Estimating Mini Mental State Examination Scores using Game-Specific Performance Values: A Preliminary Study.

Individuals with permanent cognitive impairment need to be evaluated and monitored. There exists a number of clinically validated cognitive assessment tools, but they often need to be administered by trained therapists in clinical settings. This serves as a major barrier for frequent, longitudinal monitoring of cognitive function.

The Use of a Finger-Worn Accelerometer for Monitoring of Hand Use in Ambulatory Settings.

Objective assessment of stroke survivors' upper limb movements in ambulatory settings can provide clinicians with important information regarding the real impact of rehabilitation outside the clinic and help to establish individually-tailored therapeutic programs. This paper explores a novel approach to monitor the amount of hand use, which is relevant to the purposeful, goal-directed use of the limbs, based on a body networked sensor system composed of miniaturized finger- and wrist-worn accelerometers. The main contributions of this paper are twofold.