Managing Patients with Parkinson's Disease

Home-based monitoring and management of Parkinsonian symptoms using wearable sensors can provide clinicians with detailed information to improve the quality of the life of Parkinson's Disease (PD) patients. There is an increasing interest in assessment and analysis of PD gait using wearable sensors and developing platforms that can be deployed at patients' homes to obtain daily characteristics of PD gait. With advances in miniaturised wearable sensor technologies, it is possible to create a reliable system for home-based monitoring of PD patients. Current research is directed towards creating such monitoring systems based on multiple sensor configurations that are validated in laboratories. A new low cost approach is being developed at Imperial College using the Sensixa's e-AR sensor to establish a new home-based monitoring system. The main objectives are characterising and monitoring gait in PD patients in free-living environments. The system can also be used for gait assessment of elderly populations. In a collaborative research with the Institute of Neurology, University College London (UCL), the Sensixa and the Hamlyn team are investigating the relationship between gait parameters and deep brain stimulation (DBS) settings for PD patients after surgery. From the early results, we have demonstrated that the miniaturised low cost ear worn sensor can capture the subtle changes in PD gait after adjustment and modification of DBS parameters. This pervasive sensing approach could enable the development of a closed-loop system and optimise the effect of DBS to improve patient outcomes.

Rehabilitation after Orthopaedic Surgery

More than 165,000 hip and knee replacements are carried out each year in the UK. In addition to the high cost of the  surgical operation , there is an extended burden to the NHS for continuing care and rehabilitation. With the aim of transforming the expensive and time consuming lab-based gait assessment to low cost continuous monitoring with pervasive sensing technology, a new assessment platform based on the Sensxia's e-AR sensor is being developed.  The e-AR sensor is a bio-inspired sensor. By putting the sensor on the ear, the e-AR sensor can emulate the sensory function of the human vestibular system and capture detailed posture, balance, gait and activity information of the user. A pilot study was conducted to assess the feasibility of using the low-cost, light-weight sensor to quantify the recovery. Ten patients undergoing total knee replacement were recruited. Their walking patterns were captured pre-operatively and up to 24 weeks post-operatively using the e-AR sensor. The results showed that patient recovery can be classified using data clustering techniques and wavelet decomposition of the e-AR sensor signals.

Diabetic Peripheral Neuropathy

Diabetic peripheral neuropathy (DPN) is the most common type of neuropathy in diabetic patients It is observed in 12.3% of individuals at diagnosis of diabetic metillus (DM), with this number increasing to up to 50% after 12 years of DM. There are more than 2.9 million diabetic patients in the UK, and over 70% of them have DPN. The NHS in England has spent over £600 million each year for the care of DPN patients. Early diagnosis and intervention can prevent foot ulcers and amputation, and could lead to significant reduction of NHS expenditure on diabetic patient management. A significant drive in the management of patients with DPN is improved diagnosis and accurate prediction with continuous monitoring.  By developing a novel, easy to wear miniaturised smart sensor that incorporates both peripheral blood flow and gait analysis, we are able to provide continuous, rather than sporadic measurements, allowing patient-specific risk stratification. We have used wearable sensors to objectively and quantitatively measure the gait changes in DPN with different degrees of severity. By studying gait of the DPN patients using this ear-worn system, we can use the sensor in the screening process of DPN patients with more sensitive, objective, and accurate results to aid diagnosis of DPN. We have already demonstrated that quantitative gait features extracted from a light-weight, easy to wear, single ear worn inertial sensor provide excellent separability for screening DPN patients.