Unconstrained Activities Research Articles

Comparison of vestibular input statistics during natural activities and while piloting an aircraft.

The present study investigates the statistics and spectral content of natural vestibular stimuli experienced by healthy human subjects during three unconstrained activities. More specifically, we assessed how the characteristics of vestibular inputs are altered during the operation of a complex human-machine interface (a flight in a helicopter simulator) compared with more ecological tasks, namely a walk in an office space and a seated visual exploration task. As previously reported, we found that the power spectra of vestibular stimuli experienced during self-navigation could be modeled by two power laws but noted a potential effect of task intensity on the transition frequency between the two fits. In contrast, both tasks that required a seated position had power spectra that were better described by an inverted U shape in all planes of motion. Taken together, our results suggest that 1) walking elicits stereotyped vestibular inputs whose power spectra can be modeled by two power laws that intersect at a task intensity-dependent frequency; 2) body posture induces changes in the frequency content of vestibular information; 3) pilots tend to operate their aircraft in a way that does not generate highly nonecological vestibular stimuli; and 4) nevertheless, human-machine interfaces used as a means of manual navigation still impose some unnatural, contextual constraints on their operators.NEW & NOTEWORTHY Building upon previously published research, this study assesses and compares the vestibular stimuli experienced by healthy subjects in natural tasks and during the interaction with a complex machine: a helicopter simulator. Our results suggest the existence of an anatomical filter, meaning that body posture shapes vestibular spectral content. Our findings further indicate that operators control their machine within a constrained operating range such that they experience vestibular stimulations that are as ecological as possible.

“Literacy is everywhere!”: using digital technology to broaden how parents view the home literacy environment

Parent–child engagement in constrained (e.g., learning alphabet letters or sounds) and unconstrained (e.g., storytelling, defining words) literacy activities are central components of the home literacy environment. Unconstrained activities are particularly important for children’s oral language and support school readiness. Yet, many parents do not recognize that unconstrained activities can promote learning. This study examines whether an immersive tool can help parents recognize the importance of unconstrained activities and how to embed learning into everyday home routines. N = 76 middle- to lower-income parents of three-year-old children were randomly assigned to a control condition or immersive “literacy scavenger hunt” where they self-explored opportunities to engage in unconstrained activities within everyday routines. The scavenger hunt was associated with an increase in parents’ endorsement of unconstrained activities and verbal communication within parent–child interactions. Results are discussed in terms of intervention approaches that build adult capacity around supporting children’s early literacy.

Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer.

Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.

Reliability of upper limb movement quality metrics during everyday tasks

BackgroundQuantitative assessments of an individual’s functional status commonly involve the use of movement quality metrics. Research questionThe purpose of this work was to quantify the reliability of movement quality metrics in healthy adults during a variety of unconstrained activities of daily living (ADLs). MethodsNineteen participants performed six ADLs (lifting a laundry basket, applying deodorant, turning a doorknob, placing a pill in a pillbox, placing a pushpin in a bulletin board, and drinking water from a glass) during two separate sessions. The ADLs were divided into reaching and object manipulation phases. Movement quality for each phase was assessed using three measures of smoothness (log dimensionless jerk, spectral arc length, and number of submovements) and one measure of straightness (index of curvature). Within- and between-session reliability was quantified using intraclass correlation coefficients (ICCs) and minimum detectable changes in measured units and as a percentage of their mean value (MDC%). ResultsReliability was generally lower within-session than between-session and for object manipulation tasks compared to reaching tasks. The ICCs exceeded 0.75 for 5% of the within-session metrics and 73% of the between-session metrics. The average MDC% was 35% for the within-session metrics and 20% for the between-session metrics. Reliability was similar for most metrics when averaged across the tasks, but the number of submovements consistently indicated much lower reliability. SignificanceUnconstrained ADLs can reliably be used to assess movement quality in functional settings that mimic real-world challenges. However, the specific movement quality metrics used in the assessment should be chosen carefully since some metrics perform dissimilarly when applied to the same data. In particular, it may be advisable to use the number of submovements in combination with other metrics, if it is to be used at all.

Dynamical learning and tracking of tremor and dyskinesia from wearable sensors.

We have developed and evaluated several dynamical machine-learning algorithms that were designed to track the presence and severity of tremor and dyskinesia with 1-s resolution by analyzing signals collected from Parkinson's disease (PD) patients wearing small numbers of hybrid sensors with both 3-D accelerometeric and surface-electromyographic modalities. We tested the algorithms on a 44-h signal database built from hybrid sensors worn by eight PD patients and four healthy subjects who carried out unscripted and unconstrained activities of daily living in an apartment-like environment. Comparison of the performance of our machine-learning algorithms against independent clinical annotations of disorder presence and severity demonstrates that, despite their differing approaches to dynamic pattern classification, dynamic neural networks, dynamic support vector machines, and hidden Markov models were equally effective in keeping error rates of the dynamic tracking well below 10%. A common set of experimentally derived signal features were used to train the algorithm without the need for subject-specific learning. We also found that error rates below 10% are achievable even when our algorithms are tested on data from a sensor location that is different from those used in algorithm training.

Motion sensor dyskinesia assessment during activities of daily living.

Dyskinesia throughout the levodopa dose cycle has been previously measured in patients with Parkinson's disease (PD) using a wrist-worn motion sensor during the stationary tasks of arms resting and extended. Quantifying dyskinesia during unconstrained activities poses a unique challenge since these involuntary movements are kinematically similar to voluntary movement. To determine the feasibility of using motion sensors to measure dyskinesia during activities of daily living. Fifteen PD subjects performed scripted activities of daily living while wearing motion sensors on bilateral hands, thighs, and ankles over the course of a levodopa dose cycle. Videos were scored by clinicians using the modified Abnormal Involuntary Movement Scale to rate dyskinesia severity in separate body regions, with the total score used as an overall measure. Kinematic features were extracted from the motion data and algorithms were generated to output severity scores. Movements when subjects were experiencing dyskinesia were less smooth than when they were not experiencing dyskinesia. Dyskinesia scores predicted by the model using all sensors were highly correlated with clinician scores, with a correlation coefficient of 0.86 and normalized root-mean-square-error of 7.4%. Accurate predictions were maintained when two sensors on the most affected side of the body (one on the upper extremity and one on the lower extremity) were used. A system with motion sensors may provide an accurate measure of overall dyskinesia that can be used to monitor patients as they complete typical activities, and thus provide insight on symptom fluctuation in the context of daily life.

Continuous in-home monitoring of essential tremor

BackgroundEssential tremor (ET) is typically measured in the clinic with subjective tremor rating scales which require the presence of a clinician for scoring and are not appropriate for measuring severity throughout the day. Motion sensors can accurately rate tremor severity during a set of predefined tasks in a laboratory. MethodsWe evaluated the ability of motion sensors to quantify tremor during unconstrained activities at home. 20 ET subjects wore a wireless sensor continuously for up to 10 h daily on two days and completed hourly standardized tremor assessments involving pre-defined tasks. Mathematical models were used to predict tremor rating scores from the sensor data. ResultsAt home tremor scores from hourly standardized assessments correlated with at home tremor scores estimated during unconstrained activities immediately following the standardized assessments. The hourly standardized assessments did not significantly fluctuate throughout the day, while fluctuations in the continuous assessments tended to follow changes in voluntary activity level. Both types of tremor ratings (standardized and continuous) showed high day-to-day test-retest reliability with intraclass correlation coefficients ranging from 0.67 to 0.90 for continuous ratings and 0.77 to 0.95 for standardized ratings. ConclusionsResults demonstrate the feasibility of continuous monitoring of tremor severity at home, which should provide clinicians with a measure of the temporal pattern of tremor in the context of daily life and serve as a useful tool for the evaluation of novel anti-tremor medications in clinical trials.

High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity.

Parkinson's disease (PD) can present with a variety of motor disorders that fluctuate throughout the day, making assessment a challenging task. Paper-based measurement tools can be burdensome to the patient and clinician and lack the temporal resolution needed to accurately and objectively track changes in motor symptom severity throughout the day. Wearable sensor-based systems that continuously monitor PD motor disorders may help to solve this problem, although critical shortcomings persist in identifying multiple disorders at high temporal resolution during unconstrained activity. The purpose of this study was to advance the current state of the art by (1) introducing hybrid sensor technology to concurrently acquire surface electromyographic (sEMG) and accelerometer data during unconstrained activity and (2) analyzing the data using dynamic neural network algorithms to capture the evolving temporal characteristics of the sensor data and improve motor disorder recognition of tremor and dyskinesia. Algorithms were trained (n=11 patients) and tested (n=8 patients; n=4 controls) to recognize tremor and dyskinesia at 1-second resolution based on sensor data features and expert annotation of video recording during 4-hour monitoring periods of unconstrained daily activity. The algorithms were able to make accurate distinctions between tremor, dyskinesia, and normal movement despite the presence of diverse voluntary activity. Motor disorder severity classifications averaged 94.9% sensitivity and 97.1% specificity based on 1 sensor per symptomatic limb. These initial findings indicate that new sensor technology and software algorithms can be effective in enhancing wearable sensor-based system performance for monitoring PD motor disorders during unconstrained activities.

Quantitative assessment of four men using above-elbow prosthetic control

We studied the relationship between kinematically unconstrained activities of daily living (ADL) tasks and a kinematically constrained task in above-elbow (AE) amputee subjects using myoelectrically controlled prostheses. Four men, 24 to 49 years old, with unilateral AE amputation wore a prosthesis interfaced to a programmable controller to emulate two different elbow control schemes, conventional velocity and a new "natural" controller. Subjects were timed during three ADL tasks--cutting meat, donning socks, and rolling dough--with both controllers. The prosthesis emulator was then connected to a crank device with a handle, and the subjects turned the crank from bottom to top positions in a vertical plane using each controller. Synergistic shoulder-elbow joint coordination required for crank turning was quantified as the maximum slope of the change in elbow torque versus the change in crank-angle. Performance between the two controllers differed significantly for the crank test but not for ADL tasks. One subject did not complete all crank turning tests. Positive canonical correlation of 0.77 was found between time and crank domain measures. We conclude that biomechanical assessments should be integrated with time-based clinical tests to comprehensively evaluate performance of AE amputee subjects with a myoelectric device.