Moderate-intensity cardiovascular exercise performed before motor practice attenuates offline implicit motor learning in stroke survivors but not age-matched neurotypical adults.

The acute impact of cardiovascular exercise on implicit motor learning of stroke survivors is still unknown. We investigated the effects of cardiovascular exercise on implicit motor learning of mild-moderately impaired chronic stroke survivors and neurotypical adults. We addressed whether exercise priming effects are time-dependent (e.g., exercise before or after practice) in the encoding (acquisition) and recall (retention) phases. Forty-five stroke survivors and 45 age-matched neurotypical adults were randomized into three sub-groups: BEFORE (exercise, then motor practice), AFTER (motor practice, then exercise), and No-EX (motor practice alone). All sub-groups practiced a serial reaction time task (five repeated and two pseudorandom sequences per day) on three consecutive days, followed 7 days later by a retention test (one repeated sequence). Exercise was performed on a stationary bike, (one 20-min bout per day) at 50% to 70% heart rate reserve. Implicit motor learning was measured as a difference score (repeated-pseudorandom sequence response time) during practice (acquisition) and recall (delayed retention). Separate analyses were performed on the stroke and neurotypical groups using linear mixed-effects models (participant ID was a random effect). There was no exercise-induced benefit on implicit motor learning for any sub-group. However, exercise performed before practice impaired encoding in neurotypical adults and attenuated retention performance of stroke survivors. There is no benefit to implicit motor learning of moderately intense cardiovascular exercise for stroke survivors or age-matched neurotypical adults, regardless of timing. Practice under a high arousal state and exercise-induced fatigue may have attenuated offline learning in stroke survivors.

Quantifying intra- and interlimb use during unimanual and bimanual tasks in persons with hemiparesis post-stroke.

BACKGROUND: Individuals with hemiparesis post-stroke often have difficulty with tasks requiring upper extremity (UE) intra- and interlimb use, yet methods to quantify both are limited. OBJECTIVE: To develop a quantitative yet sensitive method to identify distinct features of UE intra- and interlimb use during task performance. METHODS: Twenty adults post-stroke and 20 controls wore five inertial sensors (wrists, upper arms, sternum) during 12 seated UE tasks. Three sensor modalities (acceleration, angular rate of change, orientation) were examined for three metrics (peak to peak amplitude, time, and frequency). To allow for comparison between sensor data, the resultant values were combined into one motion parameter, per sensor pair, using a novel algorithm. This motion parameter was compared in a group-by-task analysis of variance as a similarity score (0-1) between key sensor pairs: sternum to wrist, wrist to wrist, and wrist to upper arm. A use ratio (paretic/non-paretic arm) was calculated in persons post-stroke from wrist sensor data for each modality and compared to scores from the Adult Assisting Hand Assessment (Ad-AHA Stroke) and UE Fugl-Meyer (UEFM). RESULTS: A significant group × task interaction in the similarity score was found for all key sensor pairs. Post-hoc tests between task type revealed significant differences in similarity for sensor pairs in 8/9 comparisons for controls and 3/9 comparisons for persons post stroke. The use ratio was significantly predictive of the Ad-AHA Stroke and UEFM scores for each modality. CONCLUSIONS: Our algorithm and sensor data analyses distinguished task type within and between groups and were predictive of clinical scores. Future work will assess reliability and validity of this novel metric to allow development of an easy-to-use app for clinicians.

Is there no "I" in team? Potential bias in key informant interviews when asking individuals to represent a collective perspective.

This paper sought to understand the extent to which, and how individuals use personal or collective language when asked to articulate sense of place from a collective perspective. Understanding a collective sense of place could illuminate place-based connections in natural resource industries, where it is as groups or as institutions that organizations interact with the environment rather than as individuals. While there are well known methods for collecting information about sense of place at the individual level, there is a gap in understanding the best method to collect information at a collective level. We examined the use of key-informant interviews as a method to understand collective sense of place. In Bocas del Toro, Panama, ecotourism and environmentally based organizations are becoming more prolific due to abundant natural resources, making it an interesting case study for understanding sense of place from an organizational perspective. The use of personal and collective language is examined though in-depth semi-structured interviews from 15 environmentally-oriented organizations with a total of 17 interviews. This study specifically examined whether and how key informants, when prompted to speak for their organization, spoke collectively, reflecting a collective perspective versus their own. Methods included both quantitative analysis of personal versus collective language use frequency, and qualitative examinations of how individuals used personal versus collective language. Our results indicated no difference in the frequency with which individuals use personal versus collective language. We found that how individuals situated their perspectives into an organization reflects a complex personal and collective point of view reflecting five themes of personal versus collective language use: 1) sole personal perspective, 2) sole collective perspective, 3) distinction between collective and personal perspective; 4) organization perspective with insertion of "I think"; and 5) personal and collective perspective about organization and greater community. Our research identifies a previously undiscussed potential bias of key informant interviews. These findings have implications for how researchers approach collecting information beyond the individual level.

Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson's Disease: A Novel Deep One-Class Classifier.

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson's disease. FoG impairs walking and is associated with increased fall risk. On-demand external cueing systems can detect FoG and provide stimuli to help individuals overcome freezing. Predicting FoG before onset enables preemptive cueing and may prevent FoG. However, detection and prediction remain challenging. If FoG data are not available for an individual, patient-independent models have been used to detect FoG, which have shown great sensitivity and poor specificity, or vice versa. In this study, we introduce a Deep Gait Anomaly Detector (DGAD) using a transfer learning-based approach to improve FoG detection accuracy. We also evaluate the effect of data augmentation and additional pre-FoG segments on prediction rate. Seven individuals with PD performed a series of daily walking tasks wearing inertial measurement units on their ankles. The DGAD algorithm demonstrated average sensitivity and specificity of 63.0% and 98.6% (3.2% improvement compared with the highest specificity in the literature). The target models identified 87.4% of FoG onsets, with 21.9% predicted. This study demonstrates our algorithm's potential for accurate identification of FoG and delivery of cues for patients for whom no FoG data is available for model training.

mHealth technologies used to capture walking and arm use behavior in adult stroke survivors: a scoping review beyond measurement properties.

PURPOSE: We aimed to provide a critical review of measurement properties of mHealth technologies used for stroke survivors to measure the amount and intensity of functional skills, and to identify facilitators and barriers toward adoption in research and clinical practice. MATERIALS AND METHODS: Using Arksey and O'Malley's framework, two independent reviewers determined eligibility and performed data extraction. We conducted an online consultation survey exercise with 37 experts. RESULTS: Sixty-four out of 1380 studies were included. A majority reported on lower limb behavior (n = 32), primarily step count (n = 21). Seventeen studies reported on arm-hand behaviors. Twenty-two studies reported metrics of intensity, 10 reported on energy expenditure. Reliability and validity were the most frequently reported properties, both for commercial and non-commercial devices. Facilitators and barriers included: resource costs, technical aspects, perceived usability, and ecological legitimacy. Two additional categories emerged from the survey: safety and knowledge, attitude, and clinical skill. CONCLUSIONS: This provides an initial foundation for a field experiencing rapid growth, new opportunities and the promise that mHealth technologies affords for envisioning a better future for stroke survivors. We synthesized findings into a set of recommendations for clinicians and clinician-scientists about how best to choose mHealth technologies for one's individual objective.Implications for RehabilitationRehabilitation professionals are encouraged to consider the measurement properties of those technologies that are used to monitor functional locomotor and object-interaction skills in the stroke survivors they serve.Multi-modal knowledge translation strategies (research synthesis, educational courses or videos, mentorship from experts, etc.) are available to rehabilitation professionals to improve knowledge, attitude, and skills pertaining to mHealth technologies.Consider the selection of commercially available devices that are proven to be valid, reliable, accurate, and responsive to the targeted clinical population.Consider usability and privacy, confidentiality and safety when choosing a specific device or smartphone application.

Classifying Unimanual and Bimanual Upper Extremity Tasks in Individuals Post-Stroke.

After stroke, many individuals develop impairments that lead to compensatory motions. Compensation allows individuals to achieve tasks but has long-term detrimental effects and represents maladaptive motor strategies. Increased use of bimanual motions may serve as a biomarker for recovery (and the reduction of reliance on compensatory motion), and tracking such motion using sensor data may provide critical data for health care specialists. However, past work by the authors demonstrated individual variation in motor strategies results in noisy and chaotic sensor data. The goal of the current work is to develop classifiers capable of differentiating unimanual, bimanaual asymmetric, and bimanual symmetric gestures using wearable sensor data. Twenty participants post-stroke (and 20 age-matched controls) performed a set of tasks under the supervision of a trained occupational therapist. Sensor data were recorded for each task. Classifiers were developed using artificial neural networks (ANNs) as a baseline, and the echo state neural network (ESNN) which has demonstrated efficacy with chaotic data. We find that, for control and post-stroke participants, the ESNN results in improved testing accuracy performance (91.3% and 80.3%, respectively). These results suggest a novel method for classifying gestures in individuals post-stroke, and the developed classifiers may facilitate longitudinal monitoring and correction of compensatory motion.

Comparison of Machine Learning approaches for Classifying Upper Extremity Tasks in Individuals Post-Stroke.

After a stroke, individuals often exhibit upper extremity (UE) motor dysfunction, influencing the performance of everyday tasks. Characterizing UE movements is useful to track recovery and response to intervention. Yet, due to the complexity of the recovery process, UE movements may be extremely variable and person-specific. While this renders automatic recognition of these gestures challenging, machine learning methods could be used to classify UE movements in atypical populations. In the current study, we utilize data from 20 individuals post-stroke and 20 age-matched controls to identify an optimal set of sensor-extracted features for the classification of unimanual and bimanual gestures during task performance. We found that using fewer than 100 features along with a random forest classifier produced the best performance across both groups, with both user-dependent and user-independent models.

Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson's Disease: Addressing the Class Imbalance Problem.

Freezing of gait (FoG) is a common motor symptom in patients with Parkinson's disease (PD). FoG impairs gait initiation and walking and increases fall risk. Intelligent external cueing systems implementing FoG detection algorithms have been developed to help patients recover gait after freezing. However, predicting FoG before its occurrence enables preemptive cueing and may prevent FoG. Such prediction remains challenging given the relative infrequency of freezing compared to non-freezing events. In this study, we investigated the ability of individual and ensemble classifiers to predict FoG. We also studied the effect of the ADAptive SYNthetic (ADASYN) sampling algorithm and classification cost on classifier performance. Eighteen PD patients performed a series of daily walking tasks wearing accelerometers on their ankles, with nine experiencing FoG. The ensemble classifier formed by Support Vector Machines, K-Nearest Neighbors, and Multi-Layer Perceptron using bagging techniques demonstrated highest performance (F1 = 90.7) when synthetic FoG samples were added to the training set and class cost was set as twice that of normal gait. The model identified 97.4% of the events, with 66.7% being predicted. This study demonstrates our algorithm's potential for accurate prediction of gait events and the provision of preventive cueing in spite of limited event frequency.

Prediction of Freezing of Gait in Parkinson's Disease Using Statistical Inference and Lower-Limb Acceleration Data.

The freezing of gait (FoG) is a common type of motor dysfunction in advanced Parkinson's disease (PD) associated with falls. Over the last decade, a significant amount of studies has been focused on detecting FoG episodes in clinical and home environments. Yet, there remains a paucity of techniques regarding real-time prediction of FoG before its occurrence. In this paper, a new algorithm was employed to define the best combination of sensor position, axis, sampling window length, and features to predict FoG. We hypothesized that gait deterioration before FoG onsets can be discriminated from normal gait using statistical analysis of features from successive windows of data collected from lower-limb accelerometers. We defined a new performance measure, "predictivity", to compare the number of correctly predicted FoG events among different combinations. We characterized the system performance using data from 10 PD patients, who experienced FoG while performing several walking tasks in a lab environment. The analysis of 120 different combinations revealed that prediction of FoG can be realized by using an individual shank sensor and sample entropy calculated from the horizontal forward axis with window length of 2 s (88.8%, 92.5%, and 89.0% for average predictivity, sensitivity, and specificity, respectively).

Development of Sensor-Based Measures of Upper Extremity Interlimb Coordination.

The development of motor impairment after the onset of an injury such as stroke may result in long-term compensatory behaviors. Because compensation often evolves in ambient settings (outside the purview of monitoring clinicians), there is a need for quantitative tools capable of accurately detecting the subtleties of compensation and related reduction in interlimb coordination. Improvement in interlimb coordination may serve as a marker of recovery from stroke, and rehabilitation progress. The current study investigates measures of upper extremity interlimb coordination in persons post-stroke and healthy controls. It introduces a novel algorithm for objective characterization of interlimb coordination during the performance of real-world tasks.

Detecting Reach to Grasp Activities using Motion and Muscle Activation Data.

The performance of activities of daily living (ADLs) is directly related to recovery of motor function after an incident such as stroke. Because the recovery process occurs primarily in the home, many efforts have sought to capture gross body motion and limb motion using wearable sensors. One component of function not easily quantified but nonetheless important is the ability to interact with the environment using the upper extremities. In particular, environmental interaction requires the performance of reach-to-grasp (RTG) tasks. The goal of the proposed approach is to determine the extent to which the commercial Myo armband sensor provides a noninvasive mechanism for monitoring and recording RTG task performance. Our results indicated that accelerometer and rate gyroscope data varied significantly between task types, and that a classifier using motion and muscle activation data was capable of distinguishing between gestures with 93% accuracy.

Relative Efficacy of Sensor Modalities for Estimating Post-Stroke Motor Impairment.

Wearable inertial sensing has been beneficial in the development of measures of motor impairment after stroke. While most early work focused on the use of accelerometry, recent work has increasingly shown that rate gyroscopes may provide complementary information. Differences in performance of accelerometers and gyroscopes in activity recognition may be due to the nature of the impairment. The current approach seeks to investigate the relative sensitivity of these sensor modalities to impairment by evaluating their classification accuracy for tasks adapted from the Fugl-Meyer Assessment. Our findings indicated that, for upper-extremity motion, classifiers trained using a combination of accelerometer and rate gyroscope data performed the best (accuracy of 73.1%). Classifiers trained using accelerometer data alone and rate gyroscope data alone performed slightly worse than the combined data classifier (70.2% and 65.7%, respectively).

Muscle Activation and Inertial Motion Data for Noninvasive Classification of Activities of Daily Living.

OBJECTIVE: Remote monitoring of physical activity using body-worn sensors provides an objective alternative to current functional assessment tools. The purpose of this study was to assess the feasibility of classifying categories of activities of daily living from the functional arm activity behavioral observation system (FAABOS) using muscle activation and motion data. METHODS: Ten nondisabled, healthy adults were fitted with a Myo armband on the upper forearm. This multimodal commercial sensor device features surface electromyography (sEMG) sensors, an accelerometer, and a rate gyroscope. Participants performed 17 different activities of daily living, which belonged to one of four functional groups according to the FAABOS. Signal magnitude area (SMA) and mean values were extracted from the acceleration and angular rate of change data; root mean square (RMS) was computed for the sEMG data. A nearest neighbors machine learning algorithm was then applied to predict the FAABOS task category using these raw data as inputs. RESULTS: Mean acceleration, SMA of acceleration, mean angular rate of change, and RMS of sEMG were significantly different across the four FAABOS categories ( in all cases). A classifier using mean acceleration, mean angular rate of change, and sEMG data was able to predict task category with 89.2% accuracy. CONCLUSION: The results demonstrate the feasibility of using a combination of sEMG and motion data to noninvasively classify types of activities of daily living. SIGNIFICANCE: This approach may be useful for quantifying daily activity performance in ambient settings as a more ecologically valid measure of function in healthy and disease-affected individuals.

Detecting postural transitions: a robust wavelet-based approach.

The ability to perform postural transitions such as sit-to-stand is an accepted metric for functional independence. The number of transitions performed in real-life situations provides clinically useful information for individuals recovering from lower extremity injury or surgery. Performance deficits during these transitions are well correlated to negative outcomes in numerous populations. Thus, continuous monitoring and detection of transitions in individuals outside of the clinical setting may provide important, clinically relevant information regarding the progression of physical impairments. In this paper, we propose a new inertial-sensor based approach to detecting transitions utilizing the wavelet transform. This approach performs robustly in both supervised laboratory settings, and in ambient settings. We evaluate the performance of our algorithm on a data set including 334 in-laboratory and 20 in-home postural transitions from individuals with and without motor impairments.

Upper extremity post-stroke motion quality estimation with decision trees and bagging forests.

Stroke is one of the leading causes of long-term disability. Approximately two thirds of stroke survivors require long-term rehabilitation, which suggests the importance of understanding movement quality in real-world settings. To address this need, we have developed an approach that quantifies physical activity and also evaluates performance quality. Accelerometer and gyroscope sensor data are used to measure upper extremity movements and to develop a mathematical framework to relate objective sensor data to clinical performance metrics. In this article we employ two approaches to extract clinically meaningful quality measures from individuals post-stroke; we then compare the resulting predictive ability of the two approaches. Our findings indicate that Bootstrap Aggregating forest approaches may be superior to the computationally simpler decision trees for unstable data sets including those derived from individuals post-stroke.

Relative accuracy of time and frequency domain features to quantify upper extremity coordination.

Upper limb coordination is necessary for the performance of activities of daily living (ADLs). This coordination is impaired in individuals suffering from motor deficits. The evolution of inter- and intra-limb coordination patterns may provide insight into mechanisms of recovery. In this paper, we investigate the ability of inertial sensors to capture measures of limb coordination in non-disabled individuals during the performance of ADL inspired tasks. We evaluate limb coordination as measured by time and frequency domain features extracted from inertial sensors for a subset of upper limb tasks, and evaluate the relative sensitivity of these measures to different task types.

Predicting daily gait behaviors after anterior cruciate ligament surgery: A case study.

After surgical interventions such as anterior cruciate ligament reconstruction (ACLr), people exhibit altered gait mechanics due to joint impairments. Persistence of altered mechanics after resolution of impairments may be related to daily reinforcement of maladaptive behavior. Quantifying the contribution of such maladaptive motor strategies requires continuous monitoring of locomotor behaviors in the home setting. In this paper, we investigate an inertial sensor based approach to monitoring ambient activities. We evaluate the relative performance of our predictive algorithm on one control and one individual post-ACL reconstruction.

Attentional Demand of a Virtual Reality-Based Reaching Task in Nondisabled Older Adults.

Attention during exercise is known to affect performance; however, the attentional demand inherent to virtual reality (VR)-based exercise is not well understood. We used a dual-task paradigm to compare the attentional demands of VR-based and non-VR-based (conventional, real-world) exercise: 22 non-disabled older adults performed a primary reaching task to virtual and real targets in a counterbalanced block order while verbally responding to an unanticipated auditory tone in one third of the trials. The attentional demand of the primary reaching task was inferred from the voice response time (VRT) to the auditory tone. Participants' engagement level and task experience were also obtained using questionnaires. The virtual target condition was more attention demanding (significantly longer VRT) than the real target condition. Secondary analyses revealed a significant interaction between engagement level and target condition on attentional demand. For participants who were highly engaged, attentional demand was high and independent of target condition. However, for those who were less engaged, attentional demand was low and depended on target condition (i.e., virtual > real). These findings add important knowledge to the growing body of research pertaining to the development and application of technology-enhanced exercise for elders and for rehabilitation purposes.

Spectral analyses of wrist motion in individuals poststroke: the development of a performance measure with promise for unsupervised settings.

BACKGROUND: Upper extremity use in daily life is a critical ingredient of continued functional recovery after cerebral stroke. However, time-evolutions of use-dependent motion quality are poorly understood due to limitations of existing measurement tools. OBJECTIVE: Proof-of-concept study to determine if spectral analyses explain the variability of known temporal kinematic movement quality (ie, movement duration, number of peaks, jerk) for uncontrolled reach-to-grasp tasks. METHODS: Ten individuals with chronic stroke performed unimanual goal-directed movements using both hands, with and without task object present, wearing accelerometers on each wrist. Temporal and spectral measures were extracted for each gesture. The effects of performance condition on outcome measures were determined using 2-way, within subject, hand (nonparetic vs paretic) × object (present vs absent) analysis of variance. Regression analyses determined if spectral measures explained the variability of the temporal measures. RESULTS: There were main effects of hand on all 3 temporal measures and main effects of object on movement duration and peaks. For the paretic limb, spectral measures explain 41.2% and 51.1% of the variability in movement duration and peaks, respectively. For the nonparetic limb, spectral measures explain 40.1%, 42.5%, and 27.8% of the variability of movement duration, peaks, and jerk, respectively. CONCLUSIONS: Spectral measures explain the variability of motion efficiency and control in individuals with stroke. Signal power from 1.0 to 2.0 Hz is sensitive to changes in hand and object. Analyzing the evolution of this measure in ambient environments may provide as yet uncharted information useful for evaluating long-term recovery.