Human motion analysis is a valuable tool for assessing disease progression in persons with conditions such as multiple sclerosis or Parkinson’s disease. Human motion tracking is also used extensively for sporting technique and performance analysis as well as for work life ergonomics evaluations. Wearable inertial sensors (e.g., accelerometers, gyroscopes and/or magnetometers) are frequently employed because they are easy to mount and can be used in real life, out-of-the-lab-settings, as opposed to video-based lab setups. These distributed sensors cannot, however, measure relative distances between sensors, and are also cumbersome when it comes to calibration and drift compensation. In this study, we tested an ultrasonic time-of-flight sensor for measuring relative limb-to-limb distance, and we developed a combined inertial sensor and ultrasonic time-of-flight wearable measurement system. The aim was to investigate if ultrasonic time-of-flight sensors can supplement inertial sensor-based motion tracking by providing relative distances between inertial sensor modules. We found that the ultrasonic time-of-flight measurements reflected expected walking motion patterns. The stride length estimates derived from ultrasonic time-of-flight measurements corresponded well with estimates from validated inertial sensors, indicating that the inclusion of ultrasonic time-of-flight measurements could be a feasible approach for improving inertial sensor-only systems. Our prototype was able to measure both inertial and time-of-flight measurements simultaneously and continuously, but more work is necessary to merge the complementary approaches to provide more accurate and more detailed human motion tracking.

The ability to optimize power generation in sports is imperative, both for understanding and balancing training load correctly, and for optimizing competition performance. In this paper, we aim to estimate mechanical power output by employing a time-sequential information-based deep Long Short-Term Memory (LSTM) neural network from multiple inertial measurement units (IMUs). Thirteen athletes conducted roller ski skating trials on a treadmill with varying incline and speed. The acceleration and gyroscope data collected with the IMUs were run through statistical feature processing, before being used by the deep learning model to estimate power output. The model was thereafter used for prediction of power from test data using two approaches. First, a user-dependent case was explored, reaching a power estimation within 3.5% error. Second, a user-independent case was developed, reaching an error of 11.6% for the power estimation. Finally, the LSTM model was compared to two other machine learning models and was found to be superior. In conclusion, the user-dependent model allows for precise estimation of roller skiing power output after training the model on data from each athlete. The user-independent model provides less accurate estimation; however, the accuracy may be sufficient for providing valuable information for recreational skiers.

This paper reports on the development and testing of a wearable device intended as a component in an ambulatory system for health monitoring of physical rehabilitation patients. The device measures heart rate, skin temperature, activity level and posture on the user's chest. The wearable device has been run through a set of verification tests and the accuracy has been validated in controlled environment on 12 healthy volunteers. A long term user pilot with 5 congestive heart failure patients and their nurses was performed to test the whole system. The conclusion from the performed tests is that the developed wearable multisensor monitoring device is reliable, accurate, easy to use and fit for the purpose.

The cold and harsh climate in the High North represents a threat to safety and work performance. The aim of this study was to show that sensors integrated in clothing can provide information that can improve decision support for workers in cold climate without disturbing the user. Here, a wireless demonstrator consisting of a working jacket with integrated temperature, humidity and activity sensors has been developed. Preliminary results indicate that the demonstrator can provide easy accessible information about the thermal conditions at the site of the worker and local cooling effects of extremities. The demonstrator has the ability to distinguish between activity and rest, and enables implementation of more sophisticated sensor fusion algorithms to assess work load and pre-defined activities. This information can be used in an enhanced safety perspective as an improved tool to advice outdoor work control for workers in cold climate.

The pressure on the healthcare services is building up for several reasons. The ageing population trend, the increase in life-style related disease prevalence, as well as the increased treatment capabilities with associated general expectation all add pressure. The use of ambient healthcare technologies can alleviate the situation by enabling time and cost-efficient monitoring and follow-up of patients discharged from hospital care. We report on an ambulatory system developed for monitoring of physical rehabilitation patients. The system consists of a wearable multisensor monitoring device; a mobile phone with client application aggregating the data collected; a service-oriented-architecture based server solution; and a PC application facilitating patient follow-up by their health professional carers. The system has been tested and verified for accuracy in controlled environment trials on healthy volunteers, and also been usability tested by 5 congestive heart failure patients and their nurses. This investigation indicated that patients were able to use the system, and that nurses got an improved basis for patient follow-up.

Sensor-based monitoring of congestive heart-failure (CHF) patients living at home can improve quality of care, detect exacerbations of disease at an earlier stage and motivate the patient for better self care. This paper reports on a usability study of the ESUMS system that provides continuous measurements of heart rate, activity, upper body posture and skin temperature via a sensor belt and a smartphone as patient terminal. Five CHF patients were included in the trial, all recently discharged from hospital. The nurses experienced continuous heart rate, activity and posture monitoring as useful and objective tools that helped them in their daily assessment of patient health. They also saw the system as an important educational tool to help patients gain insight into their own condition. Three patients liked that they could have a view of their own physiological and activity data, however the smartphones used in the study turned out to be too complicated for the patients to operate. A smartphone is built to be a multi-purpose device, and this may (conceptually and practically) be incompatible with the patients' demands for ease of use.