Marc Bächlin

In this paper, we present a wearable assistant for Parkinson's disease (PD) patients with the freezing of gait (FOG) symptom. This wearable system uses on-body acceleration sensors to measure the patients' movements. It automatically detects FOG by analyzing frequency components inherent in these movements. When FOG is detected, the assistant provides a rhythmic auditory signal that stimulates the patient to resume walking. Ten PD patients tested the system while performing several walking tasks in the laboratory. More than 8 h of data were recorded. Eight patients experienced FOG during the study, and 237 FOG events were identified by professional physiotherapists in a post hoc video analysis. Our wearable assistant was able to provide online assistive feedback for PD patients when they experienced FOG. The system detected FOG events online with a sensitivity of 73.1% and a specificity of 81.6%. The majority of patients indicated that the context-aware automatic cueing was beneficial to them. Finally, we characterize the system performance with respect to the walking style, the sensor placement, and the dominant algorithm parameters.

In this paper we introduce the concept of a wearable assistant for swimmer, called SwimMaster. The SwimMaster consists of acceleration sensors with micro-controllers and feedback interface modules that swimmer wear while swimming. With four different evaluation studies and a total of 22 subjects we demonstrate the functionality and power of the SwimMaster system. We show how a wide range of swim parameters can be monitored and used for a continuous swim performance evaluation. These parameters include the time per lane, the swimming velocity and the number of strokes per lane. Also swim style specific factors like the body balance and the body rotation are extracted. Finally three feedback modalities are tested and evaluated. With these means we show the ability of the SwimMaster to assist a swimmer in achieving the desired exercise goals by constantly monitoring his/her swim performance and providing the necessary feedback to achieve the desired workout goals.

BACKGROUND: About 50% of the patients with advanced Parkinson's disease (PD) suffer from freezing of gait (FOG), which is a sudden and transient inability to walk. It often causes falls, interferes with daily activities and significantly impairs quality of life. Because gait deficits in PD patients are often resistant to pharmacologic treatment, effective non-pharmacologic treatments are of special interest. OBJECTIVES: The goal of our study is to evaluate the concept of a wearable device that can obtain real-time gait data, processes them and provides assistance based on pre-determined specifications. METHODS: We developed a real-time wearable FOG detection system that automatically provides a cueing sound when FOG is detected and which stays until the subject resumes walking. We evaluated our wearable assistive technology in a study with 10 PD patients. Over eight hours of data was recorded and a questionnaire was filled out by each patient. RESULTS: Two hundred and thirty-seven FOG events have been identified by professional physiotherapists in a post-hoc video analysis. The device detected the FOG events online with a sensitivity of 73.1% and a specificity of 81.6% on a 0.5 sec frame-based evaluation. CONCLUSIONS: With this study we show that online assistive feedback for PD patients is possible. We present and discuss the patients' and physiotherapists' perspectives on wearability and performance of the wearable assistant as well as their gait performance when using the assistant and point out the next research steps. Our results demonstrate the benefit of such a context-aware system and motivate further studies.

Freezing of gait (FOG) is a common gait deficit in advanced Parkinsonpsilas disease (PD). It is often a cause of falls, interferes with daily activities and significantly impairs quality of life. Gait deficits in PD patients are often resistant to pharmacologic treatment; therefore effective nonpharmacologic assistance is needed. In this paper we show the potential of context aware assistance for PD patients with FOG and present our first results on start and turn FOG assistance using our modular wearable research platform. We developed a real-time FOG detection system which provides external acoustic cues when FOG is detected from on-body motion sensors, until the subject resumes walking. In an evaluation study, ten PD patients tested our device. We recorded over 8 h of data. Eight patients experienced FOG during the study, and 237 FOG events have been identified by physiotherapists in a post video analysis. For the first time PD patients with the FOG syndrome were assisted by a context-aware wearable system. We report a high accuracy of freeze detection (73.1% sensitivity, 81.6% specificity, user independent). Based on subjective reports, the majority of patients indicated a benefit from the automatic cueing. We discuss how additional sensor modalities can paint a more complete view of the userpsilas context and may increase the systempsilas accuracy, decrease its latency, and eventually allow going from freeze detection to freeze preemption.