Summary Background Physical activity (PA) has been reported to be reduced in severe chronic obstructive pulmonary disease (COPD). Studies in moderate COPD are currently scarce. The aim of the present study was to investigate physical activity in daily life in patients with COPD (n = 70) and controls (n = 30). Methods A multi-center controlled study was conducted. PA was assessed using a multisensor armband device (SenseWear, BodyMedia, Pittsburgh, PA) and is reported as the average number of steps per day, and the time spent in mild and moderate physical activity. Results Patients suffered from mild (n = 9), moderate (n = 28), severe (n = 23) and very severe (n = 10) COPD. The time spent in activities with mild (80 ± 69 min vs 160 ± 89 min, p < 0.0001) and moderate intensity (24 ± 29 min vs 65 ± 70 min; p < 0.0036) was reduced in patients compared to controls. The number of steps reached 87 ± 34%, 71 ± 32%, 49 ± 34% and 29 ± 20% of control values in GOLD-stages I to IV respectively. The time spent in activities at moderate intensity was 53 ± 47%, 41 ± 45%, 31 ± 47% and 22 ± 34% of the values obtained in controls respectively with increasing GOLD-stage. These differences reached statistical significance as of GOLD stage II (p < 0.05). No differences were observed among centers. Conclusions Physical activity is reduced early in the disease progression (as of GOLD-stage II). Reductions in physical activities at moderate intensity seem to precede the reduction in the amount of physical activities at lower intensity.

It has become clear recently that the epidemic of type 2 diabetes sweeping the globe is associated with decreased levels of physical activity and an increase in obesity. Incorporating appropriate and sufficient physical activity into one's life is an essential component of achieving and maintaining a healthy weight and overall health, especially for those with type II diabetes mellitus. Regular physical activity can have a positive impact by lowering blood glucose, helping the body to be more efficient at using insulin. There are other substantial benefits for patients with diabetes, including prevention of cardiovascular disease, hyperlipidemia, hypertension, and obesity. Several complications of utilizing a self-care treatment methodology involving exercise include (1) patients may not know how much activity that they engage in and (2) health-care providers do not have objective measurements of how much activity their patients perform. However, several technological advances have brought a variety of activity monitoring devices to the market that can address these concerns. Ranging from simple pedometers to multisensor devices, the different technologies offer varying levels of accuracy, comfort, and reliability. The key notion is that by providing feedback to the patient, motivation can be increased and targets can be set and aimed toward. Although these devices are not specific to the treatment of diabetes, the importance of physical activity in treating the disease makes an understanding of these devices important. This article reviews these physical activity monitors and describes the advantages and disadvantages of each. Abbreviations: (DLW) doubly labeled water, (GPS) global positioning system, (HR) heart rate, (SWA) SenseWear armband, (TEE) total energy expenditure

In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.

A critical challenge in the creation of autonomous mobile robots is the reliable detection of moving and static obstacles. In this paper, we present a passive vision system that recovers coarse depth information reliably and efficiently. This system is based on the concept of depth from focus, and robustly locates static and moving obstacles as well as stairs and dropoffs with adequate accuracy for obstacle avoidance. We describe an implementation of this vision system on a mobile robot as well as real-world experiments both indoors and outdoors. These experiments have involved several hours of continuous and fully autonomous operation in crowded, natural settings.

Adequate glycemic control in people with diabetes decreases the risk of developing both microvascular and macrovascular complications. 1,2 To achieve adequate glycemic control, frequent self-monitoring of blood glucose (SMBG) is often necessary. The American Diabetes Association recommends that individuals with diabetes monitor their blood glucose as frequently as necessary to safely manage their condition, often as many as 4 to 6 times daily. 3 The current standard method for SMBG biochemically measures capillary glucose with a portable meter and requires lancing the skin to obtain blood. Although this method is often numerically and clinically accurate, it has shortcomings. Lancing the skin is often associated with pain, which discourages long-term compliance. Also, glucose meter technology relies on episodic testing and thus requires a motivated individual to remember to test several times a day. There are continuous glucose monitoring devices (CGM) available for continuous estimation of plasma glucose concentration, but these require insertion of a cannula into the skin and thus are still invasive. Clearly, there is a need for alternative device technologies, which ideally should be noninvasive, painless, and easy to use while providing reasonable estimations of plasma glucose concentrations to make informed decisions on diabetes self-management. 4,5 Even when glucose estimates are not perfectly accurate, close estimates can be of clinical value in determining glucose trends that help inform self-management.