Dave Howard

Bio: Dave Howard is an academic researcher from University of Salford. The author has contributed to research in topics: Tibia & Buttocks. The author has an hindex of 3, co-authored 5 publications receiving 581 citations.

Activity identification using body-mounted sensors — a review of classification techniques

Abstract: With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.

Are older people putting themselves at risk when using their walking frames

Abstract: Walking aids are issued to older adults to prevent falls, however, paradoxically their use has been identified as a risk factor for falling. To prevent falls, walking aids must be used in a stable manner, but it remains unknown to what extent associated clinical guidance is adhered to at home, and whether following guidance facilitates a stable walking pattern. It was the aim of this study to investigate adherence to guidance on walking frame use, and to quantify user stability whilst using walking frames. Additionally, we explored the views of users and healthcare professionals on walking aid use, and regarding the instrumented walking frames (‘Smart Walkers’) utilized in this study. This observational study used Smart Walkers and pressure-sensing insoles to investigate usage patterns of 17 older people in their home environment; corresponding video captured contextual information. Additionally, stability when following, or not, clinical guidance was quantified for a subset of users during walking in an Activities of Daily Living Flat and in a gait laboratory. Two focus groups (users, healthcare professionals) shared their experiences with walking aids and provided feedback on the Smart Walkers. Incorrect use was observed for 16% of single support periods and for 29% of dual support periods, and was associated with environmental constraints and a specific frame design feature. Incorrect use was associated with reduced stability. Participants and healthcare professionals perceived the Smart Walker technology positively. Clinical guidance cannot easily be adhered to and self-selected strategies reduce stability, hence are placing the user at risk. Current guidance needs to be improved to address environmental constraints whilst facilitating stable walking. The research is highly relevant considering the rising number of walking aid users, their increased falls-risk, and the costs of falls.

How accurately can sitting and the intensity of walking and cycling be classified using an accelerometer on the waist for the purpose of the “Global recommendations on physical activity for health”?

Abstract: The society has become more sedentary and has developed a lack of physical activity, therefore increasing health risks. Feedback is needed to change these behaviours. For this feedback, first accurate monitoring is needed: sedentary behaviour must be classified as well as the intensity of physical activity. In this report a State of the Art analysis is performed to compare different classification techniques and finally two methods, both using an accelerometer on the waist, are worked out. These methods are Integral of the Modulus of the Accelerometer (IMA) classification and a machine learning technique (MLT): support vector machine (SVM). These methods are then applied in a laboratory experiment to study their quality. A measurement setup is made to create a dataset of the following activities: standing, sitting, lying, walking (2.4 - 7.5 km/h) and cycling (10.1-19 km/h). This dataset (n=15) is analysed and classified using Matlab for both methods. The IMA method was unable to monitor sedentary behaviour, but could classify the physical activity (PA) intensity with an accuracy of 66%. The SVM method within subjects was able to monitor sedentary behaviour with an accuracy of 91±20% and the classification of the PA intensity has an accuracy of 94±5%. For between subjects the accuracies decrease to 71±13% for PA intensity accuracy and 45±35% for the sedentary behaviour classification. IMA was implemented in the old feedback system, monitoring the overall daily amount of physical activity, but can significantly be outperformed by replacing it with the current SVM implementation. At this moment however, SVM can only be used to improve the old system, it cannot yet be used to create new additions to the feedback system, such as the implementation of the feedback of the sedentary behaviour and specific physical activity intensities. New methods and properties of the current SVM system have been found that might increase the accuracies of the between subject analysis and therefore might enable SVM to become applicable for the extra feedback options.

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Abstract: Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation.

Gait analysis using wearable sensors.

Abstract: Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications.