Sensor-Based Evaluation of Physical Therapy Exercises.

Physical therapy is important for the treatment and prevention of musculoskeletal injuries, as well as recovery from surgery. In this paper, we explore techniques for automatically determining whether an exercise was performed correctly or not, based on camera images and wearable sensors. Classifiers were tested on data collected from 30 patients during normally-scheduled physical therapy appointments. We considered two lower limb exercises, and asked how well classifiers could generalize to the assessment of individuals for whom no prior data were available. We found that our classifiers performed well relative to several metrics (mean accuracy: 0.76, specificity: 0.90), but often returned low sensitivity (mean: 0.34). For one of the two exercises considered, these classifiers compared favorably with human performance.

Parkinson's Disease Tremor Detection in the Wild Using Wearable Accelerometers.

Continuous in-home monitoring of Parkinson's Disease (PD) symptoms might allow improvements in assessment of disease progression and treatment effects. As a first step towards this goal, we evaluate the feasibility of a wrist-worn wearable accelerometer system to detect PD tremor in the wild (uncontrolled scenarios). We evaluate the performance of several feature sets and classification algorithms for robust PD tremor detection in laboratory and wild settings. We report results for both laboratory data with accurate labels and wild data with weak labels. The best performance was obtained using a combination of a pre-processing module to extract information from the tremor spectrum (based on non-negative factorization) and a deep neural network for learning relevant features and detecting tremor segments. We show how the proposed method is able to predict patient self-report measures, and we propose a new metric for monitoring PD tremor (i.e., percentage of tremor over long periods of time), which may be easier to estimate the start and end time points of each tremor event while still providing clinically useful information.

Effects of Participation and Collaboration on Perceived Effectiveness of Core Public Health Functions.

OBJECTIVES: We measured the perceived effectiveness of core public health services at the community level, and we assessed whether certain system factors were associated with perceived effectiveness. METHODS: We used data from the National Longitudinal Survey of Local Public Health Systems, conducted in 1998 and 2006, to examine the effects of the contributions of health departments and the participation of other agencies to core functions on the perceived effectiveness of community delivery of core assessment, policy development, and assurance functions. RESULTS: Perceived effectiveness increased over time for all 3 core function areas (range = 41%-53% in 2006). Multivariable results showed that local health department contribution to effort was positively associated with perceived effectiveness of public health core functions. The significance of participation of individual types of agencies or organizations varied by core function areas, except for local government agencies and hospitals, which were significantly positively associated with all 3 core function areas. CONCLUSIONS: We furthered our understanding of the significance of the contributions and participations of local health departments and of other individual agencies or organizations in the perceived effectiveness of delivery of core functions.

Motor cortical correlates of arm resting in the context of a reaching task and implications for prosthetic control.

Prosthetic devices are being developed to restore movement for motor-impaired individuals. A robotic arm can be controlled based on models that relate motor-cortical ensemble activity to kinematic parameters. The models are typically built and validated on data from structured trial periods during which a subject actively performs specific movements, but real-world prosthetic devices will need to operate correctly during rest periods as well. To develop a model of motor cortical modulation during rest, we trained monkeys (Macaca mulatta) to perform a reaching task with their own arm while recording motor-cortical single-unit activity. When a monkey spontaneously put its arm down to rest between trials, our traditional movement decoder produced a nonzero velocity prediction, which would cause undesired motion when applied to a prosthetic arm. During these rest periods, a marked shift was found in individual units' tuning functions. The activity pattern of the whole population during rest (Idle state) was highly distinct from that during reaching movements (Active state), allowing us to predict arm resting from instantaneous firing rates with 98% accuracy using a simple classifier. By cascading this state classifier and the movement decoder, we were able to predict zero velocity correctly, which would avoid undesired motion in a prosthetic application. Interestingly, firing rates during hold periods followed the Active pattern even though hold kinematics were similar to those during rest with near-zero velocity. These findings expand our concept of motor-cortical function by showing that population activity reflects behavioral context in addition to the direct parameters of the movement itself.

Comparison of brain-computer interface decoding algorithms in open-loop and closed-loop control.

Neuroprosthetic devices such as a computer cursor can be controlled by the activity of cortical neurons when an appropriate algorithm is used to decode motor intention. Algorithms which have been proposed for this purpose range from the simple population vector algorithm (PVA) and optimal linear estimator (OLE) to various versions of Bayesian decoders. Although Bayesian decoders typically provide the most accurate off-line reconstructions, it is not known which model assumptions in these algorithms are critical for improving decoding performance. Furthermore, it is not necessarily true that improvements (or deficits) in off-line reconstruction will translate into improvements (or deficits) in on-line control, as the subject might compensate for the specifics of the decoder in use at the time. Here we show that by comparing the performance of nine decoders, assumptions about uniformly distributed preferred directions and the way the cursor trajectories are smoothed have the most impact on decoder performance in off-line reconstruction, while assumptions about tuning curve linearity and spike count variance play relatively minor roles. In on-line control, subjects compensate for directional biases caused by non-uniformly distributed preferred directions, leaving cursor smoothing differences as the largest single algorithmic difference driving decoder performance.

Control of a brain-computer interface without spike sorting.

Two rhesus monkeys were trained to move a cursor using neural activity recorded with silicon arrays of 96 microelectrodes implanted in the primary motor cortex. We have developed a method to extract movement information from the recorded single and multi-unit activity in the absence of spike sorting. By setting a single threshold across all channels and fitting the resultant events with a spline tuning function, a control signal was extracted from this population using a Bayesian particle-filter extraction algorithm. The animals achieved high-quality control comparable to the performance of decoding schemes based on sorted spikes. Our results suggest that even the simplest signal processing is sufficient for high-quality neuroprosthetic control.

Cortical control of a prosthetic arm for self-feeding.

Arm movement is well represented in populations of neurons recorded from the motor cortex. Cortical activity patterns have been used in the new field of brain-machine interfaces to show how cursors on computer displays can be moved in two- and three-dimensional space. Although the ability to move a cursor can be useful in its own right, this technology could be applied to restore arm and hand function for amputees and paralysed persons. However, the use of cortical signals to control a multi-jointed prosthetic device for direct real-time interaction with the physical environment ('embodiment') has not been demonstrated. Here we describe a system that permits embodied prosthetic control; we show how monkeys (Macaca mulatta) use their motor cortical activity to control a mechanized arm replica in a self-feeding task. In addition to the three dimensions of movement, the subjects' cortical signals also proportionally controlled a gripper on the end of the arm. Owing to the physical interaction between the monkey, the robotic arm and objects in the workspace, this new task presented a higher level of difficulty than previous virtual (cursor-control) experiments. Apart from an example of simple one-dimensional control, previous experiments have lacked physical interaction even in cases where a robotic arm or hand was included in the control loop, because the subjects did not use it to interact with physical objects-an interaction that cannot be fully simulated. This demonstration of multi-degree-of-freedom embodied prosthetic control paves the way towards the development of dexterous prosthetic devices that could ultimately achieve arm and hand function at a near-natural level.

Improving the design of competitive bidding in Medicare Advantage.

In 2003, Congress passed the Medicare Prescription Drug, Improvement, and Modernization Act, which required that in 2006 the Centers for Medicare and Medicaid Services (CMS) implement a system of competitive bids to set payments for the Medicare Advantage program. Managed care plans now bid for the right to enroll Medicare beneficiaries. Data from the first year of bidding suggest that imperfect competition is limiting the success of the bidding system. This article offers suggestions to improve this system based on findings from auction theory and previous government-run auctions. In particular, CMS can benefit by adjusting its system of competitive bids in four ways: credibly committing to regulations governing bidding; limiting the scope for collusion, entry deterrence, and predatory behavior among bidders; adjusting how benchmark reimbursement rates are set; and accounting for asymmetric information among bidders.

Volunteerism and social capital in policy implementation: evidence from the Long-Term Care Ombudsman Program.

We assess the link between a program's volunteer support and state social capital in the case of the joint implementation of the federal Long-Term Care (LTC) Ombudsman Program by state and federal authorities. This program, which is designed to prevent elder abuse and ensure quality care in long-term facilities, is implemented at the state and local levels and relies heavily on volunteer staff. First, we find that volunteerism is vital to the efficacy of the program's monitoring and investigative functions. Second, we find that volunteerism in this program is tied to broader level conditions of a state's social capital. Last, we discuss the implications of our findings for volunteer-based programs devolved to the states.